{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[6 15]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output will be:\n\narray(\\[6, 15\\])\n\nLet's go through it step-by-step:\n\n1. operation1 is sum, so it will sum the elements of a along the given axis (-1 is the last axis by default)\n\n1. a is a 2D array with shape (2,3). Summing along the last axis (-1) will sum over each row.\n\n1. This gives b = array(\\[6, 15\\]), which is the sum of each row of a.\n\nSo in summary, operation1 sums over the rows of a, giving the final output of array(\\[6, 15\\]).", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[5 7 9]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Based on the documentation, here are the steps:\n\n1. operation1(a, axis=0) calls the reshape function with a and axis=0.\n\n1. reshape without axis=None flattens the array by default. So a.shape = (6,)\n\n1. The sorting is done on the flattened array, so the result will be a sorted 1D array of the elements of a.\n\n1. print(b) prints this sorted 1D array, which will be \\[1, 2, 3, 4, 5, 6\\].\n\nSo the final output is:\n\narray(\\[1, 2, 3, 4, 5, 6\\])", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "3", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. `a` is initialized as a 2D array with shape (3,2).\n\n1. `operation1(a)` calls the function `mean(a)` since `a` is an array.\n\n1. `mean(a)` calculates the mean of elements along all axes by default since `axis` is not specified. It returns a new array with the same shape as input `a` containing the mean value of each element.\n\n1. Since `a` contains integer values, the output type of `mean(a)` will be float64 by default.\n\n1. The mean value of each element is (0+1+2+3+4+5+6)/7 = 3.5.\n\n1. Therefore, the final output printed will be:\n\n```\narray([[3.5, 3.5],\n       [3.5, 3.5],  \n       [3.5, 3.5]])\n```\n\nSo the output is averaging all elements along each axis and returning the result in the same shape as the input.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[0 0 0]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code is:\n\narray(\\[\\[1, 3, 5\\],\n\\[2, 4, 6\\]\\])\n\nBreaking it down step-by-step:\n\n1. We define a 2D array a with shape (3,2) and elements \\[\\[1,2\\], \\[3,4\\], \\[5,6\\]\\]\n\n1. The function operation1(a, axis=1) computes the mean along axis 1. Since axis 1 corresponds to the columns, this computes the mean of each column:\n\n   - Column 0: mean(\\[1,3,5\\]) = 3\n   - Column 1: mean(\\[2,4,6\\]) = 4\n\n1. Taking the mean collapses that axis, so the output shape is (3,).\n   It returns the array \\[\\[1,3,5\\], \\[2,4,6\\]\\] with shape (3,2) as requested.\n\nSo in summary, operation1 with axis=1 computes the column means, collapsing that axis and returning a view of the transposed output array.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[0 0 0 0]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. operation1 calls choose on the index array \\[0, 3, 1, 2\\] and the choice arrays ch.\n1. For index 0, it picks the 0th element of the 0th choice array, which is 0.\n1. For index 3, it picks the 1st element of the 1st choice array, which is 13.\n1. For index 1, it picks the 0th element of the 2nd choice array, which is 21.\n1. For index 2, it picks the 1st element of the 3rd choice array, which is 0.\n1. The output a is the array \\[0, 13, 21, 0\\].\n\nSo the final output is:\narray(\\[0, 13, 21, 0\\])", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[1 3][2 4]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output will be:\n\n\\[\\[1, 2\\],\n\\[3, 4\\]\\]\n\nBreaking it down step-by-step:\n\n1. operation1 is an alias for reshape, so it will not modify the input array.\n\n1. The input array f is a 2D array with shape (2, 2) and elements \\[\\[1, 2\\], \\[3, 4\\]\\].\n\n1. No modifications are made to the shape or elements, so the output will be the same array f.\n\nSo the output is simply the input array f without any changes.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[1 2][3 4][4 4]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code is:\n\n\\[\\[1, 2\\],\n\\[3, 4\\],\n\\[5, 6\\]\\]\n\nHere is the step-by-step analysis:\n\n1. operation1 is defined as reshape in the code base.\n\n1. reshape is called on the input array a with new_shape = (3,2)\n\n1. a already has shape (3,2), so no change happens and a view of a is returned.\n\n1. The returned a is printed, which is the original input array.\n\nSo the final output is the unchanged input array a, which has contents \\[\\[1, 2\\], \\[3, 4\\], \\[5, 6\\]\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "5", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output will be:\n\n\\[\\[1, 2\\],\n\\[3, 4\\],\n\\[5, 6\\]\\]\n\nHere are the steps:\n\n1. operation1 calls the function transpose defined in the code base.\n\n1. transpose returns the input array a without making any changes, since a is already a 2D array.\n\n1. So operation1 simply returns the input array a.\n\nSo the final output is the original input array a, without any changes.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "2", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The code calls the function operation1 on the 2D array a. From the docstring of operation1, we can see that it returns a new view of the array, so b will be a view of a with the dimensions permuted. Since no axis is specified, it will flatten the array.\n\nSo the final output will be:\n\n\\[\\[1 2 3 4 5 6\\]\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[1 2 3][4 5 6][7 8 9]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code will be:\n\n\\[3, 4, 5, 6, 7, 8, 9, 1, 2\\]\n\nHere is the step-by-step working:\n\n1. operation1(a, (3, 3)) calls the partition function on array a and kth values (3, 3)\n\n1. The partition function rearranges the elements of a such that the element at index 3 (which is value 3) is moved to its final sorted position and all smaller elements are moved before it and equal or greater elements are moved after it.\n\n1. Since the input array a is \\[1, 2, 3, 4, 5, 6, 7, 8, 9\\], after partition it becomes \\[3, 4, 5, 6, 7, 8, 9, 1, 2\\]\n\n1. This rearranged array is returned and printed.\n\nSo in summary, partition rearranges the input array to put the element at the kth position in its final sorted spot, and operation1 calls this to partition the array a at indices (3, 3), resulting in the output array shown.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[0 2 1 4 5 3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code is:\n\n\\[1, 2, 3, 5, 6, 7\\]\n\nHere is the step-by-step analysis:\n\n1. operation1 calls the function sort from the code base\n\n1. sort sorts the input array a in ascending order\n\n1. a is initialized as \\[1, 3, 2, 7, 5, 6\\]\n\n1. After sorting, a becomes \\[1, 2, 3, 5, 6, 7\\]\n\n1. This sorted a is returned by sort and assigned to b\n\n1. b is printed, displaying the sorted array \\[1, 2, 3, 5, 6, 7\\]\n\nSo the final output is the sorted version of the input array a.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[2 1 3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a, 3) performs partition on a with kth element 3. This returns \\[2, 1, 3, 8, 9, 5\\].\n\n1. operation2(b, \\[0, 1, 2\\]) performs take on b with indices \\[0, 1, 2\\]. This returns \\[2, 1, 3\\].\n\nSo the final output is:\n\n\\[2, 1, 3\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 3 9]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a, 2) performs partition on a with kth element being the 2nd element (which is 3). This returns:\n   \\[2, 1, 3, 4, 5, 6\\]\n\n1. operation2(b, \\[0, 2, 4\\]) performs take on b with indices being \\[0, 2, 4\\]. This extracts elements at those indices and returns:\n   \\[2, 5, 6\\]\n\n1. print(c) prints the final output:\n   \\[2, 5, 6\\]\n\nSo the final output is the array \\[2, 5, 6\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 5 45]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's go through this step-by-step:\n\n1. a is initialized as a 3x3 array:\n   a = \\[\\[1, 2, 3\\],\n   \\[4, 5, 6\\],\n   \\[7, 8, 9\\]\\]\n\n1. operation1 flattens the array, so b = ravel(a)\n   b = \\[1, 2, 3, 4, 5, 6, 7, 8, 9\\]\n\n1. operation2 cumulatively sums b along the axis, so\\\n   c = cumsum(b)\n   c = \\[1, 3, 6, 10, 15, 21, 28, 36, 45\\]\n\n1. operation3 ranks c, so\n   d = rank(c)\n   d = 9\n\n1. d is printed, giving the final output:\n   9\n\nSo the final output is the rank (dimension) of the cumulatively summed array, which is 9.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[0 0 1 2 4 6]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a) returns a flattened view of a, so b = \\[0, 1, 2, 0, 0, 5\\].\n\n1. operation2(b) calculates the cumulative sum along the flattened axis. So c = \\[0, 1, 3, 3, 3, 8\\].\n\n1. operation3(c) removes single-dimensional entries from the shape of c. Since c is 1D already, d is just c.\n\nSo the final output is:\n\n\\[0, 1, 3, 3, 3, 8\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "4", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a > 4, a) finds the indices of elements in a that are greater than 4. Since a = \\[1, 5, 3, 7, 2, 8, 4, 6, 9\\], the indices that satisfy this condition are \\[1, 3, 4, 6, 7\\].\n\n1. operation2 takes these indices and extracts the corresponding elements from a. This gives \\[5, 7, 8, 6, 9\\].\n\n1. print prints out the final output, which is \\[5, 7, 8, 6, 9\\].\n\nSo the final output is:\n\n\\[5, 7, 8, 6, 9\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[2 0 4 1 3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's go through this step-by-step:\n\n1. operation1 is numpy.sort, so b = sort(a) = \\[1, 2, 3, 4, 6\\]\n\n1. operation2 is numpy.cumsum, so c = cumsum(b) = \\[1, 3, 6, 10, 16\\]\n\nSo the final output is:\n\narray(\\[ 1,  3,  6, 10, 16\\])", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 3 6]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's go through this step-by-step:\n\n1. We initialize a 3D array a with shape (1, 3, 1) and values \\[\\[1\\], \\[2\\], \\[3\\]\\]\n\n1. operation1 is reshape. It flattens any dimensions of size 1. So a.shape becomes (3,).\n\n1. operation2 is sum. It sums along the given axis (default is flattened/none). So it returns the sum of all elements, which is 1 + 2 + 3 = 6.\n\nTherefore, the final output is:\n\n6", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 1 2 3 4 5 5 6 9]\n", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The code calls the operation1(a) function, which refers to the argpartition function defined above.\n\nThe argpartition function returns the indices that would sort the input array a. Since a is unsorted, it simply returns the indices \\[0, 1, 2, 3, 4, 5, 6, 7, 8\\].\n\nSo operation1(a) returns the array \\[3, 1, 4, 1, 5, 9, 2, 6, 5\\], which is the sorted ordering of elements in a based on the indices returned by argpartition.\n\nTherefore, the final output of the code is:\n\n\\[3, 1, 4, 1, 5, 9, 2, 6, 5\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 4 2 4 3 4]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a, 1, 4) performs a partition on a along the first axis (rows) and returns the partitioned array:\n   array(\\[\\[2, 3\\],\\\n   \\[5, 6\\],\n   \\[1, 4\\]\\])\n\n1. operation2(a, (2, 3)) swaps the second and third axes of the partitioned array, returning:\n   array(\\[\\[\\[2, 5, 1\\],\n   \\[3, 6, 4\\]\\]\\])\n\n1. operation3(b) returns the number of dimensions of b, which is 3.\n\n1. operation4(c) returns the flattened array c:\n   array(\\[2, 5, 1, 3, 6, 4\\])\n\n1. operation5(c\\[d\\]) returns the element at index d in the flattened array c.\n   d was returned as 3 in the previous step.\n   The element at index 3 of c is 4.\n\nTherefore, the final output is 4.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "2", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Based on the functions defined in the code base:\n\n1. operation1(arr > 3) returns the indices of elements in arr that are greater than 3. Since arr = \\[1, 2, 3, 4, 5\\], the indices returned will be \\[3, 4\\].\n\n1. operation2(arr, indices) takes the elements from arr at the given indices. Since the indices are \\[3, 4\\], it will return \\[4, 5\\].\n\n1. final_output is assigned operation2(arr\\[0\\]), which evaluates to 5.\n\nTherefore, the final output is 5.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "0", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.\n\"\"\"\nbackground for these operations:\n\"\"\"\n\n\"\"\"Module containing non-deprecated functions borrowed from Numeric.\n\"\"\"\nimport functools\nimport types\nimport warnings\n\nimport arrayLib as np\nfrom .. import VisibleDeprecationWarning\nfrom . import multiarray as mu\nfrom . import overrides\nfrom . import umath as um\nfrom . import numerictypes as nt\nfrom ._asarray import asarray, array, asanyarray\nfrom .multiarray import concatenate\nfrom . import _methods\n\n_dt_ = nt.sctype2char\n\n# functions that are methods\n__all__ = [\n    'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',\n    'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',\n    'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',\n    'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',\n    'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_',\n    'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',\n    'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',\n]\n\n_gentype = types.GeneratorType\n# save away Python sum\n_sum_ = sum\n\narray_function_dispatch = functools.partial(\n    overrides.array_function_dispatch, module='arrayLib')\n\n\n# functions that are now methods\ndef _wrapit(obj, method, *args, **kwds):\n    try:\n        wrap = obj.__array_wrap__\n    except AttributeError:\n        wrap = None\n    result = getattr(asarray(obj), method)(*args, **kwds)\n    if wrap:\n        if not isinstance(result, mu.ndarray):\n            result = asarray(result)\n        result = wrap(result)\n    return result\n\n\ndef _wrapfunc(obj, method, *args, **kwds):\n    bound = getattr(obj, method, None)\n    if bound is None:\n        return _wrapit(obj, method, *args, **kwds)\n\n    try:\n        return bound(*args, **kwds)\n    except TypeError:\n        # A TypeError occurs if the object does have such a method in its\n        # class, but its signature is not identical to that of NumPy's. This\n        # situation has occurred in the case of a downstream library like\n        # 'pandas'.\n        #\n        # Call _wrapit from within the except clause to ensure a potential\n        # exception has a traceback chain.\n        return _wrapit(obj, method, *args, **kwds)\n\n\ndef _wrapreduction(obj, ufunc, method, axis, dtype, out, **kwargs):\n    passkwargs = {k: v for k, v in kwargs.items()\n                  if v is not _NoValue}\n\n    if type(obj) is not mu.ndarray:\n        try:\n            reduction = getattr(obj, method)\n        except AttributeError:\n            pass\n        else:\n            # This branch is needed for reductions like any which don't\n            # support a dtype.\n            if dtype is not None:\n                return reduction(axis=axis, dtype=dtype, out=out, **passkwargs)\n            else:\n                return reduction(axis=axis, out=out, **passkwargs)\n\n    return ufunc.reduce(obj, axis, dtype, out, **passkwargs)\n\n\ndef _take_dispatcher(a, indices, axis=None, out=None, mode=None):\n    return (a, out)\n\n\n@array_function_dispatch(_take_dispatcher)\ndef operation2(a, indices, axis=None, out=None, mode='raise'):\n    \"\"\"\n    Take elements from an array along an axis.\n\n    When axis is not None, this function does the same thing as \"fancy\"\n    indexing (indexing arrays using arrays); however, it can be easier to use\n    if you need elements along a given axis. A call such as\n    ``operation2(arr, indices, axis=3)`` is equivalent to\n    ``arr[:,:,:,indices,...]``.\n\n    Explained without fancy indexing, this is equivalent to the following use\n    of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of\n    indices::\n\n        Ni, Nk = a.shape[:axis], a.shape[axis+1:]\n        Nj = indices.shape\n        for ii in ndindex(Ni):\n            for jj in ndindex(Nj):\n                for kk in ndindex(Nk):\n                    out[ii + jj + kk] = a[ii + (indices[jj],) + kk]\n\n    Parameters\n    ----------\n    a : array_like (Ni..., M, Nk...)\n        The source array.\n    indices : array_like (Nj...)\n        The indices of the values to extract.\n\n        .. versionadded:: 1.8.0\n\n        Also allow scalars for indices.\n    axis : int, optional\n        The axis over which to select values. By default, the flattened\n        input array is used.\n    out : ndarray, optional (Ni..., Nj..., Nk...)\n        If provided, the result will be placed in this array. It should\n        be of the appropriate shape and dtype. Note that `out` is always\n        buffered if `mode='raise'`; use other modes for better performance.\n    mode : {'raise', 'wrap', 'clip'}, optional\n        Specifies how out-of-bounds indices will behave.\n\n        * 'raise' -- raise an error (default)\n        * 'wrap' -- wrap around\n        * 'clip' -- clip to the range\n\n        'clip' mode means that all indices that are too large are replaced\n        by the index that addresses the last element along that axis. Note\n        that this disables indexing with negative numbers.\n\n    Returns\n    -------\n    out : ndarray (Ni..., Nj..., Nk...)\n        The returned array has the same type as `a`.\n\n    See Also\n    --------\n    compress : Take elements using a boolean mask\n    ndarray.take : equivalent method\n    take_along_axis : Take elements by matching the array and the index arrays\n\n    Notes\n    -----\n\n    By eliminating the inner loop in the description above, and using `s_` to\n    build simple slice objects, `take` can be expressed  in terms of applying\n    fancy indexing to each 1-d slice::\n\n        Ni, Nk = a.shape[:axis], a.shape[axis+1:]\n        for ii in ndindex(Ni):\n            for kk in ndindex(Nj):\n                out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]\n\n    For this reason, it is equivalent to (but faster than) the following use\n    of `apply_along_axis`::\n\n        out = apply_along_axis(lambda a_1d: a_1d[indices], axis, a)\n\n    Examples\n    --------\n    >>> a = [4, 3, 5, 7, 6, 8]\n    >>> indices = [0, 1, 4]\n    >>> operation2(a, indices)\n    array([4, 3, 6])\n\n    In this example if `a` is an ndarray, \"fancy\" indexing can be used.\n\n    >>> a = array(a)\n    >>> a[indices]\n    array([4, 3, 6])\n\n    If `indices` is not one dimensional, the output also has these dimensions.\n\n    >>> operation2(a, [[0, 1], [2, 3]])\n    array([[4, 3],\n           [5, 7]])\n    \"\"\"\n    return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)\n\n\ndef _reshape_dispatcher(a, newshape, order=None):\n    return (a,)\n\n\n# not deprecated --- copy if necessary, view otherwise\n@array_function_dispatch(_reshape_dispatcher)\ndef operation2(a, newshape, order='C'):\n    \"\"\"\n    Gives a new shape to an array without changing its data.\n\n    Parameters\n    ----------\n    a : array_like\n        Array to be reshaped.\n    newshape : int or tuple of ints\n        The new shape should be compatible with the original shape. If\n        an integer, then the result will be a 1-D array of that length.\n        One shape dimension can be -1. In this case, the value is\n        inferred from the length of the array and remaining dimensions.\n    order : {'C', 'F', 'A'}, optional\n        Read the elements of `a` using this index order, and place the\n        elements into the reshaped array using this index order.  'C'\n        means to read / write the elements using C-like index order,\n        with the last axis index changing fastest, back to the first\n        axis index changing slowest. 'F' means to read / write the\n        elements using Fortran-like index order, with the first index\n        changing fastest, and the last index changing slowest. Note that\n        the 'C' and 'F' options take no account of the memory layout of\n        the underlying array, and only refer to the order of indexing.\n        'A' means to read / write the elements in Fortran-like index\n        order if `a` is Fortran *contiguous* in memory, C-like order\n        otherwise.\n\n    Returns\n    -------\n    reshaped_array : ndarray\n        This will be a new view object if possible; otherwise, it will\n        be a copy.  Note there is no guarantee of the *memory layout* (C- or\n        Fortran- contiguous) of the returned array.\n\n    See Also\n    --------\n    ndarray.reshape : Equivalent method.\n\n    Notes\n    -----\n    It is not always possible to change the shape of an array without\n    copying the data. If you want an error to be raised when the data is copied,\n    you should assign the new shape to the shape attribute of the array::\n\n     >>> a = zeros((10, 2))\n\n     # A transpose makes the array non-contiguous\n     >>> b = a.T\n\n     # Taking a view makes it possible to modify the shape without modifying\n     # the initial object.\n     >>> c = b.view()\n     >>> c.shape = (20)\n     Traceback (most recent call last):\n        ...\n     AttributeError: incompatible shape for a non-contiguous array\n\n    The `order` keyword gives the index ordering both for *fetching* the values\n    from `a`, and then *placing* the values into the output array.\n    For example, let's say you have an array:\n\n    >>> a = arange(6).reshape((3, 2))\n    >>> a\n    array([[0, 1],\n           [2, 3],\n           [4, 5]])\n\n    You can think of reshaping as first raveling the array (using the given\n    index order), then inserting the elements from the raveled array into the\n    new array using the same kind of index ordering as was used for the\n    raveling.\n\n    >>> operation2(a, (2, 3)) # C-like index ordering\n    array([[0, 1, 2],\n           [3, 4, 5]])\n    >>> operation2(operation2(a), (2, 3)) # equivalent to C ravel then C reshape\n    array([[0, 1, 2],\n           [3, 4, 5]])\n    >>> operation2(a, (2, 3), order='F') # Fortran-like index ordering\n    array([[0, 4, 3],\n           [2, 1, 5]])\n    >>> operation2(operation2(a, order='F'), (2, 3), order='F')\n    array([[0, 4, 3],\n           [2, 1, 5]])\n\n    Examples\n    --------\n    >>> a = array([[1,2,3], [4,5,6]])\n    >>> operation2(a, 6)\n    array([1, 2, 3, 4, 5, 6])\n    >>> operation2(a, 6, order='F')\n    array([1, 4, 2, 5, 3, 6])\n\n    >>> operation2(a, (3,-1))       # the unspecified value is inferred to be 2\n    array([[1, 2],\n           [3, 4],\n           [5, 6]])\n    \"\"\"\n    return _wrapfunc(a, 'reshape', newshape, order=order)\n\n\ndef _choose_dispatcher(a, choices, out=None, mode=None):\n    yield a\n    for c in choices:\n        yield c\n    yield out\n\n\n@array_function_dispatch(_choose_dispatcher)\ndef operation1(a, choices, out=None, mode='raise'):\n    \"\"\"\n    Construct an array from an index array and a set of arrays to choose from.\n\n    First of all, if confused or uncertain, definitely look at the Examples -\n    in its full generality, this function is less simple than it might\n    seem from the following code description (below ndi =\n    `arrayLib.lib.index_tricks`):\n\n    ``operation1(a,c) == array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.\n\n    But this omits some subtleties.  Here is a fully general summary:\n\n    Given an \"index\" array (`a`) of integers and a sequence of `n` arrays\n    (`choices`), `a` and each choice array are first broadcast, as necessary,\n    to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =\n    0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``\n    for each `i`.  Then, a new array with shape ``Ba.shape`` is created as\n    follows:\n\n    * if ``mode=raise`` (the default), then, first of all, each element of\n      `a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that\n      `i` (in that range) is the value at the `(j0, j1, ..., jm)` position\n      in `Ba` - then the value at the same position in the new array is the\n      value in `Bchoices[i]` at that same position;\n\n    * if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)\n      integer; modular arithmetic is used to map integers outside the range\n      `[0, n-1]` back into that range; and then the new array is constructed\n      as above;\n\n    * if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)\n      integer; negative integers are mapped to 0; values greater than `n-1`\n      are mapped to `n-1`; and then the new array is constructed as above.\n\n    Parameters\n    ----------\n    a : int array\n        This array must contain integers in `[0, n-1]`, where `n` is the number\n        of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any\n        integers are permissible.\n    choices : sequence of arrays\n        Choice arrays. `a` and all of the choices must be broadcastable to the\n        same shape.  If `choices` is itself an array (not recommended), then\n        its outermost dimension (i.e., the one corresponding to\n        ``choices.shape[0]``) is taken as defining the \"sequence\".\n    out : array, optional\n        If provided, the result will be inserted into this array. It should\n        be of the appropriate shape and dtype. Note that `out` is always\n        buffered if `mode='raise'`; use other modes for better performance.\n    mode : {'raise' (default), 'wrap', 'clip'}, optional\n        Specifies how indices outside `[0, n-1]` will be treated:\n\n          * 'raise' : an exception is raised\n          * 'wrap' : value becomes value mod `n`\n          * 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1\n\n    Returns\n    -------\n    merged_array : array\n        The merged result.\n\n    Raises\n    ------\n    ValueError: shape mismatch\n        If `a` and each choice array are not all broadcastable to the same\n        shape.\n\n    See Also\n    --------\n    ndarray.choose : equivalent method\n\n    Notes\n    -----\n    To reduce the chance of misinterpretation, even though the following\n    \"abuse\" is nominally supported, `choices` should neither be, nor be\n    thought of as, a single array, i.e., the outermost sequence-like container\n    should be either a list or a tuple.\n\n    Examples\n    --------\n\n    >>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],\n    ...   [20, 21, 22, 23], [30, 31, 32, 33]]\n    >>> operation1([2, 3, 1, 0], choices\n    ... # the first element of the result will be the first element of the\n    ... # third (2+1) \"array\" in choices, namely, 20; the second element\n    ... # will be the second element of the fourth (3+1) choice array, i.e.,\n    ... # 31, etc.\n    ... )\n    array([20, 31, 12,  3])\n    >>> operation1([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)\n    array([20, 31, 12,  3])\n    >>> # because there are 4 choice arrays\n    >>> operation1([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)\n    array([20,  1, 12,  3])\n    >>> # i.e., 0\n\n    A couple examples illustrating how choose broadcasts:\n\n    >>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]\n    >>> choices = [-10, 10]\n    >>> operation1(a, choices)\n    array([[ 10, -10,  10],\n           [-10,  10, -10],\n           [ 10, -10,  10]])\n\n    >>> # With thanks to Anne Archibald\n    >>> a = array([0, 1]).reshape((2,1,1))\n    >>> c1 = array([1, 2, 3]).reshape((1,3,1))\n    >>> c2 = array([-1, -2, -3, -4, -5]).reshape((1,1,5))\n    >>> operation1(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2\n    array([[[ 1,  1,  1,  1,  1],\n            [ 2,  2,  2,  2,  2],\n            [ 3,  3,  3,  3,  3]],\n           [[-1, -2, -3, -4, -5],\n            [-1, -2, -3, -4, -5],\n            [-1, -2, -3, -4, -5]]])\n\n    \"\"\"\n    return _wrapfunc(a, 'choose', choices, out=out, mode=mode)\n\n\ndef _repeat_dispatcher(a, repeats, axis=None):\n    return (a,)\n\n\n@array_function_dispatch(_repeat_dispatcher)\ndef repeat(a, repeats, axis=None):\n    \"\"\"\n    Repeat elements of an array.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n    repeats : int or array of ints\n        The number of repetitions for each element.  `repeats` is broadcasted\n        to fit the shape of the given axis.\n    axis : int, optional\n        The axis along which to repeat values.  By default, use the\n        flattened input array, and return a flat output array.\n\n    Returns\n    -------\n    repeated_array : ndarray\n        Output array which has the same shape as `a`, except along\n        the given axis.\n\n    See Also\n    --------\n    tile : Tile an array.\n\n    Examples\n    --------\n    >>> repeat(3, 4)\n    array([3, 3, 3, 3])\n    >>> x = array([[1,2],[3,4]])\n    >>> repeat(x, 2)\n    array([1, 1, 2, 2, 3, 3, 4, 4])\n    >>> repeat(x, 3, axis=1)\n    array([[1, 1, 1, 2, 2, 2],\n           [3, 3, 3, 4, 4, 4]])\n    >>> repeat(x, [1, 2], axis=0)\n    array([[1, 2],\n           [3, 4],\n           [3, 4]])\n\n    \"\"\"\n    return _wrapfunc(a, 'repeat', repeats, axis=axis)\n\n\ndef _put_dispatcher(a, ind, v, mode=None):\n    return (a, ind, v)\n\n\n@array_function_dispatch(_put_dispatcher)\ndef put(a, ind, v, mode='raise'):\n    \"\"\"\n    Replaces specified elements of an array with given values.\n\n    The indexing works on the flattened target array. `put` is roughly\n    equivalent to:\n\n    ::\n\n        a.flat[ind] = v\n\n    Parameters\n    ----------\n    a : ndarray\n        Target array.\n    ind : array_like\n        Target indices, interpreted as integers.\n    v : array_like\n        Values to place in `a` at target indices. If `v` is shorter than\n        `ind` it will be repeated as necessary.\n    mode : {'raise', 'wrap', 'clip'}, optional\n        Specifies how out-of-bounds indices will behave.\n\n        * 'raise' -- raise an error (default)\n        * 'wrap' -- wrap around\n        * 'clip' -- clip to the range\n\n        'clip' mode means that all indices that are too large are replaced\n        by the index that addresses the last element along that axis. Note\n        that this disables indexing with negative numbers. In 'raise' mode,\n        if an exception occurs the target array may still be modified.\n\n    See Also\n    --------\n    putmask, place\n    put_along_axis : Put elements by matching the array and the index arrays\n\n    Examples\n    --------\n    >>> a = arange(5)\n    >>> put(a, [0, 2], [-44, -55])\n    >>> a\n    array([-44,   1, -55,   3,   4])\n\n    >>> a = arange(5)\n    >>> put(a, 22, -5, mode='clip')\n    >>> a\n    array([ 0,  1,  2,  3, -5])\n\n    \"\"\"\n    try:\n        put = a.put\n    except AttributeError:\n        raise TypeError(\"argument 1 must be arrayLib.ndarray, \"\n                        \"not {name}\".format(name=type(a).__name__))\n\n    return put(ind, v, mode=mode)\n\n\ndef _swapaxes_dispatcher(a, axis1, axis2):\n    return (a,)\n\n\n@array_function_dispatch(_swapaxes_dispatcher)\ndef swapaxes(a, axis1, axis2):\n    \"\"\"\n    Interchange two axes of an array.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n    axis1 : int\n        First axis.\n    axis2 : int\n        Second axis.\n\n    Returns\n    -------\n    a_swapped : ndarray\n        For NumPy >= 1.10.0, if `a` is an ndarray, then a view of `a` is\n        returned; otherwise a new array is created. For earlier NumPy\n        versions a view of `a` is returned only if the order of the\n        axes is changed, otherwise the input array is returned.\n\n    Examples\n    --------\n    >>> x = array([[1,2,3]])\n    >>> swapaxes(x,0,1)\n    array([[1],\n           [2],\n           [3]])\n\n    >>> x = array([[[0,1],[2,3]],[[4,5],[6,7]]])\n    >>> x\n    array([[[0, 1],\n            [2, 3]],\n           [[4, 5],\n            [6, 7]]])\n\n    >>> swapaxes(x,0,2)\n    array([[[0, 4],\n            [2, 6]],\n           [[1, 5],\n            [3, 7]]])\n\n    \"\"\"\n    return _wrapfunc(a, 'swapaxes', axis1, axis2)\n\n\ndef _transpose_dispatcher(a, axes=None):\n    return (a,)\n\n\n@array_function_dispatch(_transpose_dispatcher)\ndef operation1(a, axes=None):\n    \"\"\"\n    Permute the dimensions of an array.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n    axes : list of ints, optional\n        By default, reverse the dimensions, otherwise permute the axes\n        according to the values given.\n\n    Returns\n    -------\n    p : ndarray\n        `a` with its axes permuted.  A view is returned whenever\n        possible.\n\n    See Also\n    --------\n    moveaxis\n    argsort\n\n    Notes\n    -----\n    Use `transpose(a, argsort(axes))` to invert the transposition of tensors\n    when using the `axes` keyword argument.\n\n    Transposing a 1-D array returns an unchanged view of the original array.\n\n    Examples\n    --------\n    >>> x = arange(4).reshape((2,2))\n    >>> x\n    array([[0, 1],\n           [2, 3]])\n\n    >>> operation1(x)\n    array([[0, 2],\n           [1, 3]])\n\n    >>> x = ones((1, 2, 3))\n    >>> operation1(x, (1, 0, 2)).shape\n    (2, 1, 3)\n\n    \"\"\"\n    return _wrapfunc(a, 'transpose', axes)\n\n\ndef _partition_dispatcher(a, kth, axis=None, kind=None, order=None):\n    return (a,)\n\n\n@array_function_dispatch(_partition_dispatcher)\ndef operation1(a, kth, axis=-1, kind='introselect', order=None):\n    \"\"\"\n    Return a partitioned copy of an array.\n\n    Creates a copy of the array with its elements rearranged in such a\n    way that the value of the element in k-th position is in the\n    position it would be in a sorted array. All elements smaller than\n    the k-th element are moved before this element and all equal or\n    greater are moved behind it. The ordering of the elements in the two\n    partitions is undefined.\n\n    .. versionadded:: 1.8.0\n\n    Parameters\n    ----------\n    a : array_like\n        Array to be sorted.\n    kth : int or sequence of ints\n        Element index to partition by. The k-th value of the element\n        will be in its final sorted position and all smaller elements\n        will be moved before it and all equal or greater elements behind\n        it. The order of all elements in the partitions is undefined. If\n        provided with a sequence of k-th it will partition all elements\n        indexed by k-th  of them into their sorted position at once.\n    axis : int or None, optional\n        Axis along which to sort. If None, the array is flattened before\n        sorting. The default is -1, which sorts along the last axis.\n    kind : {'introselect'}, optional\n        Selection algorithm. Default is 'introselect'.\n    order : str or list of str, optional\n        When `a` is an array with fields defined, this argument\n        specifies which fields to compare first, second, etc.  A single\n        field can be specified as a string.  Not all fields need be\n        specified, but unspecified fields will still be used, in the\n        order in which they come up in the dtype, to break ties.\n\n    Returns\n    -------\n    partitioned_array : ndarray\n        Array of the same type and shape as `a`.\n\n    See Also\n    --------\n    ndarray.partition : Method to sort an array in-place.\n    argpartition : Indirect partition.\n    sort : Full sorting\n\n    Notes\n    -----\n    The various selection algorithms are characterized by their average\n    speed, worst case performance, work space size, and whether they are\n    stable. A stable sort keeps items with the same key in the same\n    relative order. The available algorithms have the following\n    properties:\n\n    ================= ======= ============= ============ =======\n       kind            speed   worst case    work space  stable\n    ================= ======= ============= ============ =======\n    'introselect'        1        O(n)           0         no\n    ================= ======= ============= ============ =======\n\n    All the partition algorithms make temporary copies of the data when\n    partitioning along any but the last axis.  Consequently,\n    partitioning along the last axis is faster and uses less space than\n    partitioning along any other axis.\n\n    The sort order for complex numbers is lexicographic. If both the\n    real and imaginary parts are non-nan then the order is determined by\n    the real parts except when they are equal, in which case the order\n    is determined by the imaginary parts.\n\n    Examples\n    --------\n    >>> a = array([3, 4, 2, 1])\n    >>> operation1(a, 3)\n    array([2, 1, 3, 4])\n\n    >>> operation1(a, (1, 3))\n    array([1, 2, 3, 4])\n\n    \"\"\"\n    if axis is None:\n        # flatten returns (1, N) for matrix, so always use the last axis\n        a = asanyarray(a).flatten()\n        axis = -1\n    else:\n        a = asanyarray(a).copy(order=\"K\")\n    a.partition(kth, axis=axis, kind=kind, order=order)\n    return a\n\n\ndef _argpartition_dispatcher(a, kth, axis=None, kind=None, order=None):\n    return (a,)\n\n\n@array_function_dispatch(_argpartition_dispatcher)\ndef argpartition(a, kth, axis=-1, kind='introselect', order=None):\n    \"\"\"\n    Perform an indirect partition along the given axis using the\n    algorithm specified by the `kind` keyword. It returns an array of\n    indices of the same shape as `a` that index data along the given\n    axis in partitioned order.\n\n    .. versionadded:: 1.8.0\n\n    Parameters\n    ----------\n    a : array_like\n        Array to sort.\n    kth : int or sequence of ints\n        Element index to partition by. The k-th element will be in its\n        final sorted position and all smaller elements will be moved\n        before it and all larger elements behind it. The order all\n        elements in the partitions is undefined. If provided with a\n        sequence of k-th it will partition all of them into their sorted\n        position at once.\n    axis : int or None, optional\n        Axis along which to sort. The default is -1 (the last axis). If\n        None, the flattened array is used.\n    kind : {'introselect'}, optional\n        Selection algorithm. Default is 'introselect'\n    order : str or list of str, optional\n        When `a` is an array with fields defined, this argument\n        specifies which fields to compare first, second, etc. A single\n        field can be specified as a string, and not all fields need be\n        specified, but unspecified fields will still be used, in the\n        order in which they come up in the dtype, to break ties.\n\n    Returns\n    -------\n    index_array : ndarray, int\n        Array of indices that partition `a` along the specified axis.\n        If `a` is one-dimensional, ``a[index_array]`` yields a partitioned `a`.\n        More generally, ``operation2_along_axis(a, index_array, axis=a)`` always\n        yields the partitioned `a`, irrespective of dimensionality.\n\n    See Also\n    --------\n    partition : Describes partition algorithms used.\n    ndarray.partition : Inplace partition.\n    argsort : Full indirect sort\n\n    Notes\n    -----\n    See `partition` for notes on the different selection algorithms.\n\n    Examples\n    --------\n    One dimensional array:\n\n    >>> x = array([3, 4, 2, 1])\n    >>> x[argpartition(x, 3)]\n    array([2, 1, 3, 4])\n    >>> x[argpartition(x, (1, 3))]\n    array([1, 2, 3, 4])\n\n    >>> x = [3, 4, 2, 1]\n    >>> array(x)[argpartition(x, 3)]\n    array([2, 1, 3, 4])\n\n    \"\"\"\n    return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order)\n\n\ndef _sort_dispatcher(a, axis=None, kind=None, order=None):\n    return (a,)\n\n\n@array_function_dispatch(_sort_dispatcher)\ndef operation1(a, axis=-1, kind=None, order=None):\n    \"\"\"\n    Return a sorted copy of an array.\n\n    Parameters\n    ----------\n    a : array_like\n        Array to be sorted.\n    axis : int or None, optional\n        Axis along which to sort. If None, the array is flattened before\n        sorting. The default is -1, which sorts along the last axis.\n    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n        Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n        and 'mergesort' use timsort or radix sort under the covers and, in general,\n        the actual implementation will vary with data type. The 'mergesort' option\n        is retained for backwards compatibility.\n\n        .. versionchanged:: 1.15.0.\n           The 'stable' option was added.\n\n    order : str or list of str, optional\n        When `a` is an array with fields defined, this argument specifies\n        which fields to compare first, second, etc.  A single field can\n        be specified as a string, and not all fields need be specified,\n        but unspecified fields will still be used, in the order in which\n        they come up in the dtype, to break ties.\n\n    Returns\n    -------\n    sorted_array : ndarray\n        Array of the same type and shape as `a`.\n\n    See Also\n    --------\n    ndarray.sort : Method to sort an array in-place.\n    argsort : Indirect sort.\n    lexsort : Indirect stable sort on multiple keys.\n    searchsorted : Find elements in a sorted array.\n    partition : Partial sort.\n\n    Notes\n    -----\n    The various sorting algorithms are characterized by their average speed,\n    worst case performance, work space size, and whether they are stable. A\n    stable sort keeps items with the same key in the same relative\n    order. The four algorithms implemented in NumPy have the following\n    properties:\n\n    =========== ======= ============= ============ ========\n       kind      speed   worst case    work space   stable\n    =========== ======= ============= ============ ========\n    'quicksort'    1     O(n^2)            0          no\n    'heapsort'     3     O(n*log(n))       0          no\n    'mergesort'    2     O(n*log(n))      ~n/2        yes\n    'timsort'      2     O(n*log(n))      ~n/2        yes\n    =========== ======= ============= ============ ========\n\n    .. note:: The datatype determines which of 'mergesort' or 'timsort'\n       is actually used, even if 'mergesort' is specified. User selection\n       at a finer scale is not currently available.\n\n    All the sort algorithms make temporary copies of the data when\n    sorting along any but the last axis.  Consequently, sorting along\n    the last axis is faster and uses less space than sorting along\n    any other axis.\n\n    The sort order for complex numbers is lexicographic. If both the real\n    and imaginary parts are non-nan then the order is determined by the\n    real parts except when they are equal, in which case the order is\n    determined by the imaginary parts.\n\n    Previous to arrayLib 1.4.0 sorting real and complex arrays containing nan\n    values led to undefined behaviour. In arrayLib versions >= 1.4.0 nan\n    values are sorted to the end. The extended sort order is:\n\n      * Real: [R, nan]\n      * Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]\n\n    where R is a non-nan real value. Complex values with the same nan\n    placements are sorted according to the non-nan part if it exists.\n    Non-nan values are sorted as before.\n\n    .. versionadded:: 1.12.0\n\n    quicksort has been changed to an introsort which will switch\n    heapsort when it does not make enough progress. This makes its\n    worst case O(n*log(n)).\n\n    'stable' automatically choses the best stable sorting algorithm\n    for the data type being sorted. It, along with 'mergesort' is\n    currently mapped to timsort or radix sort depending on the\n    data type. API forward compatibility currently limits the\n    ability to select the implementation and it is hardwired for the different\n    data types.\n\n    .. versionadded:: 1.17.0\n\n    Timsort is added for better performance on already or nearly\n    sorted data. On random data timsort is almost identical to\n    mergesort. It is now used for stable sort while quicksort is still the\n    default sort if none is chosen. For details of timsort, refer to\n    `CPython listsort.txt <https://github.com/python/cpython/blob/3.7/Objects/listsort.txt>`_.\n    'mergesort' and 'stable' are mapped to radix sort for integer data types. Radix sort is an\n    O(n) sort instead of O(n log n).\n\n    Examples\n    --------\n    >>> a = array([[1,4],[3,1]])\n    >>> operation1(a)                # sort along the last axis\n    array([[1, 4],\n           [1, 3]])\n    >>> operation1(a, axis=None)     # sort the flattened array\n    array([1, 1, 3, 4])\n    >>> operation1(a, axis=0)        # sort along the first axis\n    array([[1, 1],\n           [3, 4]])\n\n    Use the `order` keyword to specify a field to use when sorting a\n    structured array:\n\n    >>> dtype = [('name', 'S10'), ('height', float), ('age', int)]\n    >>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),\n    ...           ('Galahad', 1.7, 38)]\n    >>> a = array(values, dtype=dtype)       # create a structured array\n    >>> operation1(a, order='height')                        # doctest: +SKIP\n    array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),\n           ('Lancelot', 1.8999999999999999, 38)],\n          dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])\n\n    Sort by age, then height if ages are equal:\n\n    >>> operation1(a, order=['age', 'height'])               # doctest: +SKIP\n    array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),\n           ('Arthur', 1.8, 41)],\n          dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])\n\n    \"\"\"\n    if axis is None:\n        # flatten returns (1, N) for matrix, so always use the last axis\n        a = asanyarray(a).flatten()\n        axis = -1\n    else:\n        a = asanyarray(a).copy(order=\"K\")\n    a.sort(axis=axis, kind=kind, order=order)\n    return a\n\n\ndef _argsort_dispatcher(a, axis=None, kind=None, order=None):\n    return (a,)\n\n\n@array_function_dispatch(_argsort_dispatcher)\ndef operation1(a, axis=-1, kind=None, order=None):\n    \"\"\"\n    Returns the indices that would sort an array.\n\n    Perform an indirect sort along the given axis using the algorithm specified\n    by the `kind` keyword. It returns an array of indices of the same shape as\n    `a` that index data along the given axis in sorted order.\n\n    Parameters\n    ----------\n    a : array_like\n        Array to sort.\n    axis : int or None, optional\n        Axis along which to sort.  The default is -1 (the last axis). If None,\n        the flattened array is used.\n    kind : {'quicksort', 'mergesort', 'heapsort', 'stable'}, optional\n        Sorting algorithm. The default is 'quicksort'. Note that both 'stable'\n        and 'mergesort' use timsort under the covers and, in general, the\n        actual implementation will vary with data type. The 'mergesort' option\n        is retained for backwards compatibility.\n\n        .. versionchanged:: 1.15.0.\n           The 'stable' option was added.\n    order : str or list of str, optional\n        When `a` is an array with fields defined, this argument specifies\n        which fields to compare first, second, etc.  A single field can\n        be specified as a string, and not all fields need be specified,\n        but unspecified fields will still be used, in the order in which\n        they come up in the dtype, to break ties.\n\n    Returns\n    -------\n    index_array : ndarray, int\n        Array of indices that sort `a` along the specified `axis`.\n        If `a` is one-dimensional, ``a[index_array]`` yields a sorted `a`.\n        More generally, ``operation2_along_axis(a, index_array, axis=axis)``\n        always yields the sorted `a`, irrespective of dimensionality.\n\n    See Also\n    --------\n    sort : Describes sorting algorithms used.\n    lexsort : Indirect stable sort with multiple keys.\n    ndarray.sort : Inplace sort.\n    argpartition : Indirect partial sort.\n\n    Notes\n    -----\n    See `sort` for notes on the different sorting algorithms.\n\n    As of NumPy 1.4.0 `argsort` works with real/complex arrays containing\n    nan values. The enhanced sort order is documented in `sort`.\n\n    Examples\n    --------\n    One dimensional array:\n\n    >>> x = array([3, 1, 2])\n    >>> operation1(x)\n    array([1, 2, 0])\n\n    Two-dimensional array:\n\n    >>> x = array([[0, 3], [2, 2]])\n    >>> x\n    array([[0, 3],\n           [2, 2]])\n\n    >>> ind = operation1(x, axis=0)  # sorts along first axis (down)\n    >>> ind\n    array([[0, 1],\n           [1, 0]])\n    >>> operation2_along_axis(x, ind, axis=0)  # same as operation1(x, axis=0)\n    array([[0, 2],\n           [2, 3]])\n\n    >>> ind = operation1(x, axis=1)  # sorts along last axis (across)\n    >>> ind\n    array([[0, 1],\n           [0, 1]])\n    >>> operation2_along_axis(x, ind, axis=1)  # same as operation1(x, axis=1)\n    array([[0, 3],\n           [2, 2]])\n\n    Indices of the sorted elements of a N-dimensional array:\n\n    >>> ind = unravel_index(operation1(x, axis=None), x.shape)\n    >>> ind\n    (array([0, 1, 1, 0]), array([0, 0, 1, 1]))\n    >>> x[ind]  # same as operation1(x, axis=None)\n    array([0, 2, 2, 3])\n\n    Sorting with keys:\n\n    >>> x = array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])\n    >>> x\n    array([(1, 0), (0, 1)],\n          dtype=[('x', '<i4'), ('y', '<i4')])\n\n    >>> operation1(x, order=('x','y'))\n    array([1, 0])\n\n    >>> operation1(x, order=('y','x'))\n    array([0, 1])\n\n    \"\"\"\n    return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)\n\n\ndef _argmax_dispatcher(a, axis=None, out=None):\n    return (a, out)\n\n\n@array_function_dispatch(_argmax_dispatcher)\ndef argmax(a, axis=None, out=None):\n    \"\"\"\n    Returns the indices of the maximum values along an axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n    axis : int, optional\n        By default, the index is into the flattened array, otherwise\n        along the specified axis.\n    out : array, optional\n        If provided, the result will be inserted into this array. It should\n        be of the appropriate shape and dtype.\n\n    Returns\n    -------\n    index_array : ndarray of ints\n        Array of indices into the array. It has the same shape as `a.shape`\n        with the dimension along `axis` removed.\n\n    See Also\n    --------\n    ndarray.argmax, argmin\n    amax : The maximum value along a given axis.\n    unravel_index : Convert a flat index into an index tuple.\n\n    Notes\n    -----\n    In case of multiple occurrences of the maximum values, the indices\n    corresponding to the first occurrence are returned.\n\n    Examples\n    --------\n    >>> a = arange(6).reshape(2,3) + 10\n    >>> a\n    array([[10, 11, 12],\n           [13, 14, 15]])\n    >>> argmax(a)\n    5\n    >>> argmax(a, axis=0)\n    array([1, 1, 1])\n    >>> argmax(a, axis=1)\n    array([2, 2])\n\n    Indexes of the maximal elements of a N-dimensional array:\n\n    >>> ind = unravel_index(argmax(a, axis=None), a.shape)\n    >>> ind\n    (1, 2)\n    >>> a[ind]\n    15\n\n    >>> b = arange(6)\n    >>> b[1] = 5\n    >>> b\n    array([0, 5, 2, 3, 4, 5])\n    >>> argmax(b)  # Only the first occurrence is returned.\n    1\n\n    \"\"\"\n    return _wrapfunc(a, 'argmax', axis=axis, out=out)\n\n\ndef _argmin_dispatcher(a, axis=None, out=None):\n    return (a, out)\n\n\n@array_function_dispatch(_argmin_dispatcher)\ndef operation1(a, axis=None, out=None):\n    \"\"\"\n    Returns the indices of the minimum values along an axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n    axis : int, optional\n        By default, the index is into the flattened array, otherwise\n        along the specified axis.\n    out : array, optional\n        If provided, the result will be inserted into this array. It should\n        be of the appropriate shape and dtype.\n\n    Returns\n    -------\n    index_array : ndarray of ints\n        Array of indices into the array. It has the same shape as `a.shape`\n        with the dimension along `axis` removed.\n\n    See Also\n    --------\n    ndarray.argmin, argmax\n    amin : The minimum value along a given axis.\n    unravel_index : Convert a flat index into an index tuple.\n\n    Notes\n    -----\n    In case of multiple occurrences of the minimum values, the indices\n    corresponding to the first occurrence are returned.\n\n    Examples\n    --------\n    >>> a = arange(6).reshape(2,3) + 10\n    >>> a\n    array([[10, 11, 12],\n           [13, 14, 15]])\n    >>> operation1(a)\n    0\n    >>> operation1(a, axis=0)\n    array([0, 0, 0])\n    >>> operation1(a, axis=1)\n    array([0, 0])\n\n    Indices of the minimum elements of a N-dimensional array:\n\n    >>> ind = unravel_index(operation1(a, axis=None), a.shape)\n    >>> ind\n    (0, 0)\n    >>> a[ind]\n    10\n\n    >>> b = arange(6) + 10\n    >>> b[4] = 10\n    >>> b\n    array([10, 11, 12, 13, 10, 15])\n    >>> operation1(b)  # Only the first occurrence is returned.\n    0\n\n    \"\"\"\n    return _wrapfunc(a, 'argmin', axis=axis, out=out)\n\n\ndef _searchsorted_dispatcher(a, v, side=None, sorter=None):\n    return (a, v, sorter)\n\n\n@array_function_dispatch(_searchsorted_dispatcher)\ndef searchsorted(a, v, side='left', sorter=None):\n    \"\"\"\n    Find indices where elements should be inserted to maintain order.\n\n    Find the indices into a sorted array `a` such that, if the\n    corresponding elements in `v` were inserted before the indices, the\n    order of `a` would be preserved.\n\n    Assuming that `a` is sorted:\n\n    ======  ============================\n    `side`  returned index `i` satisfies\n    ======  ============================\n    left    ``a[i-1] < v <= a[i]``\n    right   ``a[i-1] <= v < a[i]``\n    ======  ============================\n\n    Parameters\n    ----------\n    a : 1-D array_like\n        Input array. If `sorter` is None, then it must be sorted in\n        ascending order, otherwise `sorter` must be an array of indices\n        that sort it.\n    v : array_like\n        Values to insert into `a`.\n    side : {'left', 'right'}, optional\n        If 'left', the index of the first suitable location found is given.\n        If 'right', return the last such index.  If there is no suitable\n        index, return either 0 or N (where N is the length of `a`).\n    sorter : 1-D array_like, optional\n        Optional array of integer indices that sort array a into ascending\n        order. They are typically the result of argsort.\n\n        .. versionadded:: 1.7.0\n\n    Returns\n    -------\n    indices : array of ints\n        Array of insertion points with the same shape as `v`.\n\n    See Also\n    --------\n    sort : Return a sorted copy of an array.\n    histogram : Produce histogram from 1-D data.\n\n    Notes\n    -----\n    Binary search is used to find the required insertion points.\n\n    As of NumPy 1.4.0 `searchsorted` works with real/complex arrays containing\n    `nan` values. The enhanced sort order is documented in `sort`.\n\n    This function uses the same algorithm as the builtin python `bisect.bisect_left`\n    (``side='left'``) and `bisect.bisect_right` (``side='right'``) functions,\n    which is also vectorized in the `v` argument.\n\n    Examples\n    --------\n    >>> searchsorted([1,2,3,4,5], 3)\n    2\n    >>> searchsorted([1,2,3,4,5], 3, side='right')\n    3\n    >>> searchsorted([1,2,3,4,5], [-10, 10, 2, 3])\n    array([0, 5, 1, 2])\n\n    \"\"\"\n    return _wrapfunc(a, 'searchsorted', v, side=side, sorter=sorter)\n\n\ndef _resize_dispatcher(a, new_shape):\n    return (a,)\n\n\n@array_function_dispatch(_resize_dispatcher)\ndef operation1(a, new_shape):\n    \"\"\"\n    Return a new array with the specified shape.\n\n    If the new array is larger than the original array, then the new\n    array is filled with repeated copies of `a`.  Note that this behavior\n    is different from a.resize(new_shape) which fills with zeros instead\n    of repeated copies of `a`.\n\n    Parameters\n    ----------\n    a : array_like\n        Array to be resized.\n\n    new_shape : int or tuple of int\n        Shape of resized array.\n\n    Returns\n    -------\n    reshaped_array : ndarray\n        The new array is formed from the data in the old array, repeated\n        if necessary to fill out the required number of elements.  The\n        data are repeated in the order that they are stored in memory.\n\n    See Also\n    --------\n    ndarray.resize : resize an array in-place.\n\n    Notes\n    -----\n    Warning: This functionality does **not** consider axes separately,\n    i.e. it does not apply interpolation/extrapolation.\n    It fills the return array with the required number of elements, taken\n    from `a` as they are laid out in memory, disregarding strides and axes.\n    (This is in case the new shape is smaller. For larger, see above.)\n    This functionality is therefore not suitable to resize images,\n    or data where each axis represents a separate and distinct entity.\n\n    Examples\n    --------\n    >>> a=array([[0,1],[2,3]])\n    >>> operation1(a,(2,3))\n    array([[0, 1, 2],\n           [3, 0, 1]])\n    >>> operation1(a,(1,4))\n    array([[0, 1, 2, 3]])\n    >>> operation1(a,(2,4))\n    array([[0, 1, 2, 3],\n           [0, 1, 2, 3]])\n\n    \"\"\"\n    if isinstance(new_shape, (int, nt.integer)):\n        new_shape = (new_shape,)\n    a = ravel(a)\n    Na = len(a)\n    total_size = um.multiply.reduce(new_shape)\n    if Na == 0 or total_size == 0:\n        return mu.zeros(new_shape, a.dtype)\n\n    n_copies = int(total_size / Na)\n    extra = total_size % Na\n\n    if extra != 0:\n        n_copies = n_copies + 1\n        extra = Na - extra\n\n    a = concatenate((a,) * n_copies)\n    if extra > 0:\n        a = a[:-extra]\n\n    return reshape(a, new_shape)\n\n\ndef _squeeze_dispatcher(a, axis=None):\n    return (a,)\n\n\n@array_function_dispatch(_squeeze_dispatcher)\ndef operation3(a, axis=None):\n    \"\"\"\n    Remove single-dimensional entries from the shape of an array.\n\n    Parameters\n    ----------\n    a : array_like\n        Input data.\n    axis : None or int or tuple of ints, optional\n        .. versionadded:: 1.7.0\n\n        Selects a subset of the single-dimensional entries in the\n        shape. If an axis is selected with shape entry greater than\n        one, an error is raised.\n\n    Returns\n    -------\n    squeezed : ndarray\n        The input array, but with all or a subset of the\n        dimensions of length 1 removed. This is always `a` itself\n        or a view into `a`.\n\n    Raises\n    ------\n    ValueError\n        If `axis` is not `None`, and an axis being squeezed is not of length 1\n\n    See Also\n    --------\n    expand_dims : The inverse operation, adding singleton dimensions\n    reshape : Insert, remove, and combine dimensions, and resize existing ones\n\n    Examples\n    --------\n    >>> x = array([[[0], [1], [2]]])\n    >>> x.shape\n    (1, 3, 1)\n    >>> operation3(x).shape\n    (3,)\n    >>> operation3(x, axis=0).shape\n    (3, 1)\n    >>> operation3(x, axis=1).shape\n    Traceback (most recent call last):\n    ...\n    ValueError: cannot select an axis to squeeze out which has size not equal to one\n    >>> operation3(x, axis=2).shape\n    (1, 3)\n\n    \"\"\"\n    try:\n        squeeze = a.squeeze\n    except AttributeError:\n        return _wrapit(a, 'squeeze', axis=axis)\n    if axis is None:\n        return squeeze()\n    else:\n        return squeeze(axis=axis)\n\n\ndef _diagonal_dispatcher(a, offset=None, axis1=None, axis2=None):\n    return (a,)\n\n\n@array_function_dispatch(_diagonal_dispatcher)\ndef operation1(a, offset=0, axis1=0, axis2=1):\n    \"\"\"\n    Return specified diagonals.\n\n    If `a` is 2-D, returns the diagonal of `a` with the given offset,\n    i.e., the collection of elements of the form ``a[i, i+offset]``.  If\n    `a` has more than two dimensions, then the axes specified by `axis1`\n    and `axis2` are used to determine the 2-D sub-array whose diagonal is\n    returned.  The shape of the resulting array can be determined by\n    removing `axis1` and `axis2` and appending an index to the right equal\n    to the size of the resulting diagonals.\n\n    In versions of NumPy prior to 1.7, this function always returned a new,\n    independent array containing a copy of the values in the diagonal.\n\n    In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal,\n    but depending on this fact is deprecated. Writing to the resulting\n    array continues to work as it used to, but a FutureWarning is issued.\n\n    Starting in NumPy 1.9 it returns a read-only view on the original array.\n    Attempting to write to the resulting array will produce an error.\n\n    In some future release, it will return a read/write view and writing to\n    the returned array will alter your original array.  The returned array\n    will have the same type as the input array.\n\n    If you don't write to the array returned by this function, then you can\n    just ignore all of the above.\n\n    If you depend on the current behavior, then we suggest copying the\n    returned array explicitly, i.e., use ``operation1(a).copy()`` instead\n    of just ``operation1(a)``. This will work with both past and future\n    versions of NumPy.\n\n    Parameters\n    ----------\n    a : array_like\n        Array from which the diagonals are taken.\n    offset : int, optional\n        Offset of the diagonal from the main diagonal.  Can be positive or\n        negative.  Defaults to main diagonal (0).\n    axis1 : int, optional\n        Axis to be used as the first axis of the 2-D sub-arrays from which\n        the diagonals should be taken.  Defaults to first axis (0).\n    axis2 : int, optional\n        Axis to be used as the second axis of the 2-D sub-arrays from\n        which the diagonals should be taken. Defaults to second axis (1).\n\n    Returns\n    -------\n    array_of_diagonals : ndarray\n        If `a` is 2-D, then a 1-D array containing the diagonal and of the\n        same type as `a` is returned unless `a` is a `matrix`, in which case\n        a 1-D array rather than a (2-D) `matrix` is returned in order to\n        maintain backward compatibility.\n\n        If ``a.ndim > 2``, then the dimensions specified by `axis1` and `axis2`\n        are removed, and a new axis inserted at the end corresponding to the\n        diagonal.\n\n    Raises\n    ------\n    ValueError\n        If the dimension of `a` is less than 2.\n\n    See Also\n    --------\n    diag : MATLAB work-a-like for 1-D and 2-D arrays.\n    diagflat : Create diagonal arrays.\n    trace : Sum along diagonals.\n\n    Examples\n    --------\n    >>> a = arange(4).reshape(2,2)\n    >>> a\n    array([[0, 1],\n           [2, 3]])\n    >>> a.diagonal()\n    array([0, 3])\n    >>> a.diagonal(1)\n    array([1])\n\n    A 3-D example:\n\n    >>> a = arange(8).reshape(2,2,2); a\n    array([[[0, 1],\n            [2, 3]],\n           [[4, 5],\n            [6, 7]]])\n    >>> a.diagonal(0,  # Main diagonals of two arrays created by skipping\n    ...            0,  # across the outer(left)-most axis last and\n    ...            1)  # the \"middle\" (row) axis first.\n    array([[0, 6],\n           [1, 7]])\n\n    The sub-arrays whose main diagonals we just obtained; note that each\n    corresponds to fixing the right-most (column) axis, and that the\n    diagonals are \"packed\" in rows.\n\n    >>> a[:,:,0]  # main diagonal is [0 6]\n    array([[0, 2],\n           [4, 6]])\n    >>> a[:,:,1]  # main diagonal is [1 7]\n    array([[1, 3],\n           [5, 7]])\n\n    The anti-diagonal can be obtained by reversing the order of elements\n    using either `arrayLib.flipud` or `arrayLib.fliplr`.\n\n    >>> a = arange(9).reshape(3, 3)\n    >>> a\n    array([[0, 1, 2],\n           [3, 4, 5],\n           [6, 7, 8]])\n    >>> fliplr(a).diagonal()  # Horizontal flip\n    array([2, 4, 6])\n    >>> flipud(a).diagonal()  # Vertical flip\n    array([6, 4, 2])\n\n    Note that the order in which the diagonal is retrieved varies depending\n    on the flip function.\n    \"\"\"\n    if isinstance(a, matrix):\n        # Make diagonal of matrix 1-D to preserve backward compatibility.\n        return asarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)\n    else:\n        return asanyarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)\n\n\ndef _trace_dispatcher(\n        a, offset=None, axis1=None, axis2=None, dtype=None, out=None):\n    return (a, out)\n\n\n@array_function_dispatch(_trace_dispatcher)\ndef operation1(a, offset=0, axis1=0, axis2=1, dtype=None, out=None):\n    \"\"\"\n    Return the sum along diagonals of the array.\n\n    If `a` is 2-D, the sum along its diagonal with the given offset\n    is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.\n\n    If `a` has more than two dimensions, then the axes specified by axis1 and\n    axis2 are used to determine the 2-D sub-arrays whose traces are returned.\n    The shape of the resulting array is the same as that of `a` with `axis1`\n    and `axis2` removed.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array, from which the diagonals are taken.\n    offset : int, optional\n        Offset of the diagonal from the main diagonal. Can be both positive\n        and negative. Defaults to 0.\n    axis1, axis2 : int, optional\n        Axes to be used as the first and second axis of the 2-D sub-arrays\n        from which the diagonals should be taken. Defaults are the first two\n        axes of `a`.\n    dtype : dtype, optional\n        Determines the data-type of the returned array and of the accumulator\n        where the elements are summed. If dtype has the value None and `a` is\n        of integer type of precision less than the default integer\n        precision, then the default integer precision is used. Otherwise,\n        the precision is the same as that of `a`.\n    out : ndarray, optional\n        Array into which the output is placed. Its type is preserved and\n        it must be of the right shape to hold the output.\n\n    Returns\n    -------\n    sum_along_diagonals : ndarray\n        If `a` is 2-D, the sum along the diagonal is returned.  If `a` has\n        larger dimensions, then an array of sums along diagonals is returned.\n\n    See Also\n    --------\n    diag, diagonal, diagflat\n\n    Examples\n    --------\n    >>> operation1(eye(3))\n    3.0\n    >>> a = arange(8).reshape((2,2,2))\n    >>> operation1(a)\n    array([6, 8])\n\n    >>> a = arange(24).reshape((2,2,2,3))\n    >>> operation1(a).shape\n    (2, 3)\n\n    \"\"\"\n    if isinstance(a, matrix):\n        # Get trace of matrix via an array to preserve backward compatibility.\n        return asarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)\n    else:\n        return asanyarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)\n\n\ndef _ravel_dispatcher(a, order=None):\n    return (a,)\n\n\n@array_function_dispatch(_ravel_dispatcher)\ndef operation2(a, order='C'):\n    \"\"\"Return a contiguous flattened array.\n\n    A 1-D array, containing the elements of the input, is returned.  A copy is\n    made only if needed.\n\n    As of NumPy 1.10, the returned array will have the same type as the input\n    array. (for example, a masked array will be returned for a masked array\n    input)\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.  The elements in `a` are read in the order specified by\n        `order`, and packed as a 1-D array.\n    order : {'C','F', 'A', 'K'}, optional\n\n        The elements of `a` are read using this index order. 'C' means\n        to index the elements in row-major, C-style order,\n        with the last axis index changing fastest, back to the first\n        axis index changing slowest.  'F' means to index the elements\n        in column-major, Fortran-style order, with the\n        first index changing fastest, and the last index changing\n        slowest. Note that the 'C' and 'F' options take no account of\n        the memory layout of the underlying array, and only refer to\n        the order of axis indexing.  'A' means to read the elements in\n        Fortran-like index order if `a` is Fortran *contiguous* in\n        memory, C-like order otherwise.  'K' means to read the\n        elements in the order they occur in memory, except for\n        reversing the data when strides are negative.  By default, 'C'\n        index order is used.\n\n    Returns\n    -------\n    y : array_like\n        y is an array of the same subtype as `a`, with shape ``(a.size,)``.\n        Note that matrices are special cased for backward compatibility, if `a`\n        is a matrix, then y is a 1-D ndarray.\n\n    See Also\n    --------\n    ndarray.flat : 1-D iterator over an array.\n    ndarray.flatten : 1-D array copy of the elements of an array\n                      in row-major order.\n    ndarray.reshape : Change the shape of an array without changing its data.\n\n    Notes\n    -----\n    In row-major, C-style order, in two dimensions, the row index\n    varies the slowest, and the column index the quickest.  This can\n    be generalized to multiple dimensions, where row-major order\n    implies that the index along the first axis varies slowest, and\n    the index along the last quickest.  The opposite holds for\n    column-major, Fortran-style index ordering.\n\n    When a view is desired in as many cases as possible, ``arr.reshape(-1)``\n    may be preferable.\n\n    Examples\n    --------\n    It is equivalent to ``reshape(-1, order=order)``.\n\n    >>> x = array([[1, 2, 3], [4, 5, 6]])\n    >>> operation2(x)\n    array([1, 2, 3, 4, 5, 6])\n\n    >>> x.reshape(-1)\n    array([1, 2, 3, 4, 5, 6])\n\n    >>> operation2(x, order='F')\n    array([1, 4, 2, 5, 3, 6])\n\n    When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:\n\n    >>> operation2(x.T)\n    array([1, 4, 2, 5, 3, 6])\n    >>> operation2(x.T, order='A')\n    array([1, 2, 3, 4, 5, 6])\n\n    When ``order`` is 'K', it will preserve orderings that are neither 'C'\n    nor 'F', but won't reverse axes:\n\n    >>> a = arange(3)[::-1]; a\n    array([2, 1, 0])\n    >>> a.ravel(order='C')\n    array([2, 1, 0])\n    >>> a.ravel(order='K')\n    array([2, 1, 0])\n\n    >>> a = arange(12).reshape(2,3,2).swapaxes(1,2); a\n    array([[[ 0,  2,  4],\n            [ 1,  3,  5]],\n           [[ 6,  8, 10],\n            [ 7,  9, 11]]])\n    >>> a.ravel(order='C')\n    array([ 0,  2,  4,  1,  3,  5,  6,  8, 10,  7,  9, 11])\n    >>> a.ravel(order='K')\n    array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])\n\n    \"\"\"\n    if isinstance(a, matrix):\n        return asarray(a).ravel(order=order)\n    else:\n        return asanyarray(a).ravel(order=order)\n\n\ndef _nonzero_dispatcher(a):\n    return (a,)\n\n\n@array_function_dispatch(_nonzero_dispatcher)\ndef operation1(a):\n    \"\"\"\n    Return the indices of the elements that are non-zero.\n\n    Returns a tuple of arrays, one for each dimension of `a`,\n    containing the indices of the non-zero elements in that\n    dimension. The values in `a` are always tested and returned in\n    row-major, C-style order.\n\n    To group the indices by element, rather than dimension, use `argwhere`,\n    which returns a row for each non-zero element.\n\n    .. note::\n        When called on a zero-d array or scalar, ``nonzero(a)`` is treated\n        as ``nonzero(atleast1d(a))``.\n\n        ..deprecated:: 1.17.0\n            Use `atleast1d` explicitly if this behavior is deliberate.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n\n    Returns\n    -------\n    tuple_of_arrays : tuple\n        Indices of elements that are non-zero.\n\n    See Also\n    --------\n    flatnonzero :\n        Return indices that are non-zero in the flattened version of the input\n        array.\n    ndarray.nonzero :\n        Equivalent ndarray method.\n    count_nonzero :\n        Counts the number of non-zero elements in the input array.\n\n    Notes\n    -----\n    While the nonzero values can be obtained with ``a[nonzero(a)]``, it is\n    recommended to use ``x[x.astype(bool)]`` or ``x[x != 0]`` instead, which\n    will correctly handle 0-d arrays.\n\n    Examples\n    --------\n    >>> x = array([[3, 0, 0], [0, 4, 0], [5, 6, 0]])\n    >>> x\n    array([[3, 0, 0],\n           [0, 4, 0],\n           [5, 6, 0]])\n    >>> operation1(x)\n    (array([0, 1, 2, 2]), array([0, 1, 0, 1]))\n\n    >>> x[operation1(x)]\n    array([3, 4, 5, 6])\n    >>> operation1(operation1(x))\n    array([[0, 0],\n           [1, 1],\n           [2, 0],\n           [2, 1]])\n\n    A common use for ``nonzero`` is to find the indices of an array, where\n    a condition is True.  Given an array `a`, the condition `a` > 3 is a\n    boolean array and since False is interpreted as 0, operation1(a > 3)\n    yields the indices of the `a` where the condition is true.\n\n    >>> a = array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n    >>> a > 3\n    array([[False, False, False],\n           [ True,  True,  True],\n           [ True,  True,  True]])\n    >>> operation1(a > 3)\n    (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))\n\n    Using this result to index `a` is equivalent to using the mask directly:\n\n    >>> a[operation1(a > 3)]\n    array([4, 5, 6, 7, 8, 9])\n    >>> a[a > 3]  # prefer this spelling\n    array([4, 5, 6, 7, 8, 9])\n\n    ``nonzero`` can also be called as a method of the array.\n\n    >>> (a > 3).nonzero()\n    (array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))\n\n    \"\"\"\n    return _wrapfunc(a, 'nonzero')\n\n\ndef _shape_dispatcher(a):\n    return (a,)\n\n\n@array_function_dispatch(_shape_dispatcher)\ndef shape(a):\n    \"\"\"\n    Return the shape of an array.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n\n    Returns\n    -------\n    shape : tuple of ints\n        The elements of the shape tuple give the lengths of the\n        corresponding array dimensions.\n\n    See Also\n    --------\n    alen\n    ndarray.shape : Equivalent array method.\n\n    Examples\n    --------\n    >>> shape(eye(3))\n    (3, 3)\n    >>> shape([[1, 2]])\n    (1, 2)\n    >>> shape([0])\n    (1,)\n    >>> shape(0)\n    ()\n\n    >>> a = array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])\n    >>> shape(a)\n    (2,)\n    >>> a.shape\n    (2,)\n\n    \"\"\"\n    try:\n        result = a.shape\n    except AttributeError:\n        result = asarray(a).shape\n    return result\n\n\ndef _compress_dispatcher(condition, a, axis=None, out=None):\n    return (condition, a, out)\n\n\n@array_function_dispatch(_compress_dispatcher)\ndef operation1(condition, a, axis=None, out=None):\n    \"\"\"\n    Return selected slices of an array along given axis.\n\n    When working along a given axis, a slice along that axis is returned in\n    `output` for each index where `condition` evaluates to True. When\n    working on a 1-D array, `compress` is equivalent to `extract`.\n\n    Parameters\n    ----------\n    condition : 1-D array of bools\n        Array that selects which entries to return. If len(condition)\n        is less than the size of `a` along the given axis, then output is\n        truncated to the length of the condition array.\n    a : array_like\n        Array from which to extract a part.\n    axis : int, optional\n        Axis along which to take slices. If None (default), work on the\n        flattened array.\n    out : ndarray, optional\n        Output array.  Its type is preserved and it must be of the right\n        shape to hold the output.\n\n    Returns\n    -------\n    compressed_array : ndarray\n        A copy of `a` without the slices along axis for which `condition`\n        is false.\n\n    See Also\n    --------\n    take, choose, diag, diagonal, select\n    ndarray.compress : Equivalent method in ndarray\n    extract: Equivalent method when working on 1-D arrays\n    arrayLib.doc.ufuncs : Section \"Output arguments\"\n\n    Examples\n    --------\n    >>> a = array([[1, 2], [3, 4], [5, 6]])\n    >>> a\n    array([[1, 2],\n           [3, 4],\n           [5, 6]])\n    >>> operation1([0, 1], a, axis=0)\n    array([[3, 4]])\n    >>> operation1([False, True, True], a, axis=0)\n    array([[3, 4],\n           [5, 6]])\n    >>> operation1([False, True], a, axis=1)\n    array([[2],\n           [4],\n           [6]])\n\n    Working on the flattened array does not return slices along an axis but\n    selects elements.\n\n    >>> operation1([False, True], a)\n    array([2])\n\n    \"\"\"\n    return _wrapfunc(a, 'compress', condition, axis=axis, out=out)\n\n\ndef _clip_dispatcher(a, a_min, a_max, out=None, **kwargs):\n    return (a, a_min, a_max)\n\n\n@array_function_dispatch(_clip_dispatcher)\ndef operation1(a, a_min, a_max, out=None, **kwargs):\n    \"\"\"\n    Clip (limit) the values in an array.\n\n    Given an interval, values outside the interval are clipped to\n    the interval edges.  For example, if an interval of ``[0, 1]``\n    is specified, values smaller than 0 become 0, and values larger\n    than 1 become 1.\n\n    Equivalent to but faster than ``maximum(a_min, minimum(a, a_max))``.\n    No check is performed to ensure ``a_min < a_max``.\n\n    Parameters\n    ----------\n    a : array_like\n        Array containing elements to clip.\n    a_min : scalar or array_like or `None`\n        Minimum value. If `None`, clipping is not performed on lower\n        interval edge. Not more than one of `a_min` and `a_max` may be\n        `None`.\n    a_max : scalar or array_like or `None`\n        Maximum value. If `None`, clipping is not performed on upper\n        interval edge. Not more than one of `a_min` and `a_max` may be\n        `None`. If `a_min` or `a_max` are array_like, then the three\n        arrays will be broadcasted to match their shapes.\n    out : ndarray, optional\n        The results will be placed in this array. It may be the input\n        array for in-place clipping.  `out` must be of the right shape\n        to hold the output.  Its type is preserved.\n    **kwargs\n        For other keyword-only arguments, see the\n        :ref:`ufunc docs <ufuncs.kwargs>`.\n\n        .. versionadded:: 1.17.0\n\n    Returns\n    -------\n    clipped_array : ndarray\n        An array with the elements of `a`, but where values\n        < `a_min` are replaced with `a_min`, and those > `a_max`\n        with `a_max`.\n\n    See Also\n    --------\n    arrayLib.doc.ufuncs : Section \"Output arguments\"\n\n    Examples\n    --------\n    >>> a = arange(10)\n    >>> operation1(a, 1, 8)\n    array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])\n    >>> a\n    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n    >>> operation1(a, 3, 6, out=a)\n    array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])\n    >>> a = arange(10)\n    >>> a\n    array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])\n    >>> operation1(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)\n    array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])\n\n    \"\"\"\n    return _wrapfunc(a, 'clip', a_min, a_max, out=out, **kwargs)\n\n\ndef _sum_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,\n                    initial=None, where=None):\n    return (a, out)\n\n\n@array_function_dispatch(_sum_dispatcher)\ndef operation1(a, axis=None, dtype=None, out=None, keepdims=_NoValue,\n        initial=_NoValue, where=_NoValue):\n    \"\"\"\n    Sum of array elements over a given axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Elements to sum.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which a sum is performed.  The default,\n        axis=None, will sum all of the elements of the input array.  If\n        axis is negative it counts from the last to the first axis.\n\n        .. versionadded:: 1.7.0\n\n        If axis is a tuple of ints, a sum is performed on all of the axes\n        specified in the tuple instead of a single axis or all the axes as\n        before.\n    dtype : dtype, optional\n        The type of the returned array and of the accumulator in which the\n        elements are summed.  The dtype of `a` is used by default unless `a`\n        has an integer dtype of less precision than the default platform\n        integer.  In that case, if `a` is signed then the platform integer\n        is used while if `a` is unsigned then an unsigned integer of the\n        same precision as the platform integer is used.\n    out : ndarray, optional\n        Alternative output array in which to place the result. It must have\n        the same shape as the expected output, but the type of the output\n        values will be cast if necessary.\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `sum` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n    initial : scalar, optional\n        Starting value for the sum. See `~arrayLib.ufunc.reduce` for details.\n\n        .. versionadded:: 1.15.0\n\n    where : array_like of bool, optional\n        Elements to include in the sum. See `~arrayLib.ufunc.reduce` for details.\n\n        .. versionadded:: 1.17.0\n\n    Returns\n    -------\n    sum_along_axis : ndarray\n        An array with the same shape as `a`, with the specified\n        axis removed.   If `a` is a 0-d array, or if `axis` is None, a scalar\n        is returned.  If an output array is specified, a reference to\n        `out` is returned.\n\n    See Also\n    --------\n    ndarray.sum : Equivalent method.\n\n    add.reduce : Equivalent functionality of `add`.\n\n    cumsum : Cumulative sum of array elements.\n\n    trapz : Integration of array values using the composite trapezoidal rule.\n\n    mean, average\n\n    Notes\n    -----\n    Arithmetic is modular when using integer types, and no error is\n    raised on overflow.\n\n    The sum of an empty array is the neutral element 0:\n\n    >>> operation1([])\n    0.0\n\n    For floating point numbers the numerical precision of sum (and\n    ``add.reduce``) is in general limited by directly adding each number\n    individually to the result causing rounding errors in every step.\n    However, often arrayLib will use a  numerically better approach (partial\n    pairwise summation) leading to improved precision in many use-cases.\n    This improved precision is always provided when no ``axis`` is given.\n    When ``axis`` is given, it will depend on which axis is summed.\n    Technically, to provide the best speed possible, the improved precision\n    is only used when the summation is along the fast axis in memory.\n    Note that the exact precision may vary depending on other parameters.\n    In contrast to NumPy, Python's ``math.fsum`` function uses a slower but\n    more precise approach to summation.\n    Especially when summing a large number of lower precision floating point\n    numbers, such as ``float32``, numerical errors can become significant.\n    In such cases it can be advisable to use `dtype=\"float64\"` to use a higher\n    precision for the output.\n\n    Examples\n    --------\n    >>> operation1([0.5, 1.5])\n    2.0\n    >>> operation1([0.5, 0.7, 0.2, 1.5], dtype=int32)\n    1\n    >>> operation1([[0, 1], [0, 5]])\n    6\n    >>> operation1([[0, 1], [0, 5]], axis=0)\n    array([0, 6])\n    >>> operation1([[0, 1], [0, 5]], axis=1)\n    array([1, 5])\n    >>> operation1([[0, 1], [nan, 5]], where=[False, True], axis=1)\n    array([1., 5.])\n\n    If the accumulator is too small, overflow occurs:\n\n    >>> ones(128, dtype=int8).sum(dtype=int8)\n    -128\n\n    You can also start the sum with a value other than zero:\n\n    >>> operation1([10], initial=5)\n    15\n    \"\"\"\n    if isinstance(a, _gentype):\n        # 2018-02-25, 1.15.0\n        warnings.warn(\n            \"Calling operation1(generator) is deprecated, and in the future will give a different result. \"\n            \"Use operation1(fromiter(generator)) or the python sum builtin instead.\",\n            DeprecationWarning, stacklevel=3)\n\n        res = _sum_(a)\n        if out is not None:\n            out[...] = res\n            return out\n        return res\n\n    return _wrapreduction(a, add, 'sum', axis, dtype, out, keepdims=keepdims,\n                          initial=initial, where=where)\n\n\ndef _any_dispatcher(a, axis=None, out=None, keepdims=None):\n    return (a, out)\n\n\n@array_function_dispatch(_any_dispatcher)\ndef any(a, axis=None, out=None, keepdims=_NoValue):\n    \"\"\"\n    Test whether any array element along a given axis evaluates to True.\n\n    Returns single boolean unless `axis` is not ``None``\n\n    Parameters\n    ----------\n    a : array_like\n        Input array or object that can be converted to an array.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which a logical OR reduction is performed.\n        The default (`axis` = `None`) is to perform a logical OR over all\n        the dimensions of the input array. `axis` may be negative, in\n        which case it counts from the last to the first axis.\n\n        .. versionadded:: 1.7.0\n\n        If this is a tuple of ints, a reduction is performed on multiple\n        axes, instead of a single axis or all the axes as before.\n    out : ndarray, optional\n        Alternate output array in which to place the result.  It must have\n        the same shape as the expected output and its type is preserved\n        (e.g., if it is of type float, then it will remain so, returning\n        1.0 for True and 0.0 for False, regardless of the type of `a`).\n        See `doc.ufuncs` (Section \"Output arguments\") for details.\n\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `any` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n\n    Returns\n    -------\n    any : bool or ndarray\n        A new boolean or `ndarray` is returned unless `out` is specified,\n        in which case a reference to `out` is returned.\n\n    See Also\n    --------\n    ndarray.any : equivalent method\n\n    all : Test whether all elements along a given axis evaluate to True.\n\n    Notes\n    -----\n    Not a Number (NaN), positive infinity and negative infinity evaluate\n    to `True` because these are not equal to zero.\n\n    Examples\n    --------\n    >>> any([[True, False], [True, True]])\n    True\n\n    >>> any([[True, False], [False, False]], axis=0)\n    array([ True, False])\n\n    >>> any([-1, 0, 5])\n    True\n\n    >>> any(nan)\n    True\n\n    >>> o=array(False)\n    >>> z=any([-1, 4, 5], out=o)\n    >>> z, o\n    (array(True), array(True))\n    >>> # Check now that z is a reference to o\n    >>> z is o\n    True\n    >>> id(z), id(o) # identity of z and o              # doctest: +SKIP\n    (191614240, 191614240)\n\n    \"\"\"\n    return _wrapreduction(a, logical_or, 'any', axis, None, out, keepdims=keepdims)\n\n\ndef _all_dispatcher(a, axis=None, out=None, keepdims=None):\n    return (a, out)\n\n\n@array_function_dispatch(_all_dispatcher)\ndef all(a, axis=None, out=None, keepdims=_NoValue):\n    \"\"\"\n    Test whether all array elements along a given axis evaluate to True.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array or object that can be converted to an array.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which a logical AND reduction is performed.\n        The default (`axis` = `None`) is to perform a logical AND over all\n        the dimensions of the input array. `axis` may be negative, in\n        which case it counts from the last to the first axis.\n\n        .. versionadded:: 1.7.0\n\n        If this is a tuple of ints, a reduction is performed on multiple\n        axes, instead of a single axis or all the axes as before.\n    out : ndarray, optional\n        Alternate output array in which to place the result.\n        It must have the same shape as the expected output and its\n        type is preserved (e.g., if ``dtype(out)`` is float, the result\n        will consist of 0.0's and 1.0's).  See `doc.ufuncs` (Section\n        \"Output arguments\") for more details.\n\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `all` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n\n    Returns\n    -------\n    all : ndarray, bool\n        A new boolean or array is returned unless `out` is specified,\n        in which case a reference to `out` is returned.\n\n    See Also\n    --------\n    ndarray.all : equivalent method\n\n    any : Test whether any element along a given axis evaluates to True.\n\n    Notes\n    -----\n    Not a Number (NaN), positive infinity and negative infinity\n    evaluate to `True` because these are not equal to zero.\n\n    Examples\n    --------\n    >>> all([[True,False],[True,True]])\n    False\n\n    >>> all([[True,False],[True,True]], axis=0)\n    array([ True, False])\n\n    >>> all([-1, 4, 5])\n    True\n\n    >>> all([1.0, nan])\n    True\n\n    >>> o=array(False)\n    >>> z=all([-1, 4, 5], out=o)\n    >>> id(z), id(o), z\n    (28293632, 28293632, array(True)) # may vary\n\n    \"\"\"\n    return _wrapreduction(a, logical_and, 'all', axis, None, out, keepdims=keepdims)\n\n\ndef _cumsum_dispatcher(a, axis=None, dtype=None, out=None):\n    return (a, out)\n\n\n@array_function_dispatch(_cumsum_dispatcher)\ndef operation2(a, axis=None, dtype=None, out=None):\n    \"\"\"\n    Return the cumulative sum of the elements along a given axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n    axis : int, optional\n        Axis along which the cumulative sum is computed. The default\n        (None) is to compute the cumsum over the flattened array.\n    dtype : dtype, optional\n        Type of the returned array and of the accumulator in which the\n        elements are summed.  If `dtype` is not specified, it defaults\n        to the dtype of `a`, unless `a` has an integer dtype with a\n        precision less than that of the default platform integer.  In\n        that case, the default platform integer is used.\n    out : ndarray, optional\n        Alternative output array in which to place the result. It must\n        have the same shape and buffer length as the expected output\n        but the type will be cast if necessary. See `doc.ufuncs`\n        (Section \"Output arguments\") for more details.\n\n    Returns\n    -------\n    cumsum_along_axis : ndarray.\n        A new array holding the result is returned unless `out` is\n        specified, in which case a reference to `out` is returned. The\n        result has the same size as `a`, and the same shape as `a` if\n        `axis` is not None or `a` is a 1-d array.\n\n\n    See Also\n    --------\n    sum : Sum array elements.\n\n    trapz : Integration of array values using the composite trapezoidal rule.\n\n    diff :  Calculate the n-th discrete difference along given axis.\n\n    Notes\n    -----\n    Arithmetic is modular when using integer types, and no error is\n    raised on overflow.\n\n    Examples\n    --------\n    >>> a = array([[1,2,3], [4,5,6]])\n    >>> a\n    array([[1, 2, 3],\n           [4, 5, 6]])\n    >>> operation2(a)\n    array([ 1,  3,  6, 10, 15, 21])\n    >>> operation2(a, dtype=float)     # specifies type of output value(s)\n    array([  1.,   3.,   6.,  10.,  15.,  21.])\n\n    >>> operation2(a,axis=0)      # sum over rows for each of the 3 columns\n    array([[1, 2, 3],\n           [5, 7, 9]])\n    >>> operation2(a,axis=1)      # sum over columns for each of the 2 rows\n    array([[ 1,  3,  6],\n           [ 4,  9, 15]])\n\n    \"\"\"\n    return _wrapfunc(a, 'cumsum', axis=axis, dtype=dtype, out=out)\n\n\ndef _ptp_dispatcher(a, axis=None, out=None, keepdims=None):\n    return (a, out)\n\n\n@array_function_dispatch(_ptp_dispatcher)\ndef operation2(a, axis=None, out=None, keepdims=_NoValue):\n    \"\"\"\n    Range of values (maximum - minimum) along an axis.\n\n    The name of the function comes from the acronym for 'peak to peak'.\n\n    Parameters\n    ----------\n    a : array_like\n        Input values.\n    axis : None or int or tuple of ints, optional\n        Axis along which to find the peaks.  By default, flatten the\n        array.  `axis` may be negative, in\n        which case it counts from the last to the first axis.\n\n        .. versionadded:: 1.15.0\n\n        If this is a tuple of ints, a reduction is performed on multiple\n        axes, instead of a single axis or all the axes as before.\n    out : array_like\n        Alternative output array in which to place the result. It must\n        have the same shape and buffer length as the expected output,\n        but the type of the output values will be cast if necessary.\n\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `ptp` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n\n    Returns\n    -------\n    ptp : ndarray\n        A new array holding the result, unless `out` was\n        specified, in which case a reference to `out` is returned.\n\n    Examples\n    --------\n    >>> x = arange(4).reshape((2,2))\n    >>> x\n    array([[0, 1],\n           [2, 3]])\n\n    >>> operation2(x, axis=0)\n    array([2, 2])\n\n    >>> operation2(x, axis=1)\n    array([1, 1])\n\n    \"\"\"\n    kwargs = {}\n    if keepdims is not _NoValue:\n        kwargs['keepdims'] = keepdims\n    if type(a) is not mu.ndarray:\n        try:\n            ptp = a.ptp\n        except AttributeError:\n            pass\n        else:\n            return ptp(axis=axis, out=out, **kwargs)\n    return _methods._ptp(a, axis=axis, out=out, **kwargs)\n\n\ndef _amax_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,\n                     where=None):\n    return (a, out)\n\n\n@array_function_dispatch(_amax_dispatcher)\ndef amax(a, axis=None, out=None, keepdims=_NoValue, initial=_NoValue,\n         where=_NoValue):\n    \"\"\"\n    Return the maximum of an array or maximum along an axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Input data.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which to operate.  By default, flattened input is\n        used.\n\n        .. versionadded:: 1.7.0\n\n        If this is a tuple of ints, the maximum is selected over multiple axes,\n        instead of a single axis or all the axes as before.\n    out : ndarray, optional\n        Alternative output array in which to place the result.  Must\n        be of the same shape and buffer length as the expected output.\n        See `doc.ufuncs` (Section \"Output arguments\") for more details.\n\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `amax` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n\n    initial : scalar, optional\n        The minimum value of an output element. Must be present to allow\n        computation on empty slice. See `~arrayLib.ufunc.reduce` for details.\n\n        .. versionadded:: 1.15.0\n\n    where : array_like of bool, optional\n        Elements to compare for the maximum. See `~arrayLib.ufunc.reduce`\n        for details.\n\n        .. versionadded:: 1.17.0\n\n    Returns\n    -------\n    amax : ndarray or scalar\n        Maximum of `a`. If `axis` is None, the result is a scalar value.\n        If `axis` is given, the result is an array of dimension\n        ``a.ndim - 1``.\n\n    See Also\n    --------\n    amin :\n        The minimum value of an array along a given axis, propagating any NaNs.\n    nanmax :\n        The maximum value of an array along a given axis, ignoring any NaNs.\n    maximum :\n        Element-wise maximum of two arrays, propagating any NaNs.\n    fmax :\n        Element-wise maximum of two arrays, ignoring any NaNs.\n    argmax :\n        Return the indices of the maximum values.\n\n    nanmin, minimum, fmin\n\n    Notes\n    -----\n    NaN values are propagated, that is if at least one item is NaN, the\n    corresponding max value will be NaN as well. To ignore NaN values\n    (MATLAB behavior), please use nanmax.\n\n    Don't use `amax` for element-wise comparison of 2 arrays; when\n    ``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than\n    ``amax(a, axis=0)``.\n\n    Examples\n    --------\n    >>> a = arange(4).reshape((2,2))\n    >>> a\n    array([[0, 1],\n           [2, 3]])\n    >>> amax(a)           # Maximum of the flattened array\n    3\n    >>> amax(a, axis=0)   # Maxima along the first axis\n    array([2, 3])\n    >>> amax(a, axis=1)   # Maxima along the second axis\n    array([1, 3])\n    >>> amax(a, where=[False, True], initial=-1, axis=0)\n    array([-1,  3])\n    >>> b = arange(5, dtype=float)\n    >>> b[2] = NaN\n    >>> amax(b)\n    nan\n    >>> amax(b, where=~isnan(b), initial=-1)\n    4.0\n    >>> nanmax(b)\n    4.0\n\n    You can use an initial value to compute the maximum of an empty slice, or\n    to initialize it to a different value:\n\n    >>> max([[-50], [10]], axis=-1, initial=0)\n    array([ 0, 10])\n\n    Notice that the initial value is used as one of the elements for which the\n    maximum is determined, unlike for the default argument Python's max\n    function, which is only used for empty iterables.\n\n    >>> max([5], initial=6)\n    6\n    >>> max([5], default=6)\n    5\n    \"\"\"\n    return _wrapreduction(a, maximum, 'max', axis, None, out,\n                          keepdims=keepdims, initial=initial, where=where)\n\n\ndef _amin_dispatcher(a, axis=None, out=None, keepdims=None, initial=None,\n                     where=None):\n    return (a, out)\n\n\n@array_function_dispatch(_amin_dispatcher)\ndef amin(a, axis=None, out=None, keepdims=_NoValue, initial=_NoValue,\n         where=_NoValue):\n    \"\"\"\n    Return the minimum of an array or minimum along an axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Input data.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which to operate.  By default, flattened input is\n        used.\n\n        .. versionadded:: 1.7.0\n\n        If this is a tuple of ints, the minimum is selected over multiple axes,\n        instead of a single axis or all the axes as before.\n    out : ndarray, optional\n        Alternative output array in which to place the result.  Must\n        be of the same shape and buffer length as the expected output.\n        See `doc.ufuncs` (Section \"Output arguments\") for more details.\n\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `amin` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n\n    initial : scalar, optional\n        The maximum value of an output element. Must be present to allow\n        computation on empty slice. See `~arrayLib.ufunc.reduce` for details.\n\n        .. versionadded:: 1.15.0\n\n    where : array_like of bool, optional\n        Elements to compare for the minimum. See `~arrayLib.ufunc.reduce`\n        for details.\n\n        .. versionadded:: 1.17.0\n\n    Returns\n    -------\n    amin : ndarray or scalar\n        Minimum of `a`. If `axis` is None, the result is a scalar value.\n        If `axis` is given, the result is an array of dimension\n        ``a.ndim - 1``.\n\n    See Also\n    --------\n    amax :\n        The maximum value of an array along a given axis, propagating any NaNs.\n    nanmin :\n        The minimum value of an array along a given axis, ignoring any NaNs.\n    minimum :\n        Element-wise minimum of two arrays, propagating any NaNs.\n    fmin :\n        Element-wise minimum of two arrays, ignoring any NaNs.\n    argmin :\n        Return the indices of the minimum values.\n\n    nanmax, maximum, fmax\n\n    Notes\n    -----\n    NaN values are propagated, that is if at least one item is NaN, the\n    corresponding min value will be NaN as well. To ignore NaN values\n    (MATLAB behavior), please use nanmin.\n\n    Don't use `amin` for element-wise comparison of 2 arrays; when\n    ``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than\n    ``amin(a, axis=0)``.\n\n    Examples\n    --------\n    >>> a = arange(4).reshape((2,2))\n    >>> a\n    array([[0, 1],\n           [2, 3]])\n    >>> amin(a)           # Minimum of the flattened array\n    0\n    >>> amin(a, axis=0)   # Minima along the first axis\n    array([0, 1])\n    >>> amin(a, axis=1)   # Minima along the second axis\n    array([0, 2])\n    >>> amin(a, where=[False, True], initial=10, axis=0)\n    array([10,  1])\n\n    >>> b = arange(5, dtype=float)\n    >>> b[2] = NaN\n    >>> amin(b)\n    nan\n    >>> amin(b, where=~isnan(b), initial=10)\n    0.0\n    >>> nanmin(b)\n    0.0\n\n    >>> min([[-50], [10]], axis=-1, initial=0)\n    array([-50,   0])\n\n    Notice that the initial value is used as one of the elements for which the\n    minimum is determined, unlike for the default argument Python's max\n    function, which is only used for empty iterables.\n\n    Notice that this isn't the same as Python's ``default`` argument.\n\n    >>> min([6], initial=5)\n    5\n    >>> min([6], default=5)\n    6\n    \"\"\"\n    return _wrapreduction(a, minimum, 'min', axis, None, out,\n                          keepdims=keepdims, initial=initial, where=where)\n\n\ndef _alen_dispathcer(a):\n    return (a,)\n\n\n@array_function_dispatch(_alen_dispathcer)\ndef operation1(a):\n    \"\"\"\n    Return the length of the first dimension of the input array.\n\n    Parameters\n    ----------\n    a : array_like\n       Input array.\n\n    Returns\n    -------\n    alen : int\n       Length of the first dimension of `a`.\n\n    See Also\n    --------\n    shape, size\n\n    Examples\n    --------\n    >>> a = zeros((7,4,5))\n    >>> a.shape[0]\n    7\n    >>> operation1(a)\n    7\n\n    \"\"\"\n    try:\n        return len(a)\n    except TypeError:\n        return len(array(a, ndmin=1))\n\n\ndef _prod_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None,\n                     initial=None, where=None):\n    return (a, out)\n\n\n@array_function_dispatch(_prod_dispatcher)\ndef prod(a, axis=None, dtype=None, out=None, keepdims=_NoValue,\n         initial=_NoValue, where=_NoValue):\n    \"\"\"\n    Return the product of array elements over a given axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Input data.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which a product is performed.  The default,\n        axis=None, will calculate the product of all the elements in the\n        input array. If axis is negative it counts from the last to the\n        first axis.\n\n        .. versionadded:: 1.7.0\n\n        If axis is a tuple of ints, a product is performed on all of the\n        axes specified in the tuple instead of a single axis or all the\n        axes as before.\n    dtype : dtype, optional\n        The type of the returned array, as well as of the accumulator in\n        which the elements are multiplied.  The dtype of `a` is used by\n        default unless `a` has an integer dtype of less precision than the\n        default platform integer.  In that case, if `a` is signed then the\n        platform integer is used while if `a` is unsigned then an unsigned\n        integer of the same precision as the platform integer is used.\n    out : ndarray, optional\n        Alternative output array in which to place the result. It must have\n        the same shape as the expected output, but the type of the output\n        values will be cast if necessary.\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left in the\n        result as dimensions with size one. With this option, the result\n        will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `prod` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n    initial : scalar, optional\n        The starting value for this product. See `~arrayLib.ufunc.reduce` for details.\n\n        .. versionadded:: 1.15.0\n\n    where : array_like of bool, optional\n        Elements to include in the product. See `~arrayLib.ufunc.reduce` for details.\n\n        .. versionadded:: 1.17.0\n\n    Returns\n    -------\n    product_along_axis : ndarray, see `dtype` parameter above.\n        An array shaped as `a` but with the specified axis removed.\n        Returns a reference to `out` if specified.\n\n    See Also\n    --------\n    ndarray.prod : equivalent method\n    arrayLib.doc.ufuncs : Section \"Output arguments\"\n\n    Notes\n    -----\n    Arithmetic is modular when using integer types, and no error is\n    raised on overflow.  That means that, on a 32-bit platform:\n\n    >>> x = array([536870910, 536870910, 536870910, 536870910])\n    >>> prod(x)\n    16 # may vary\n\n    The product of an empty array is the neutral element 1:\n\n    >>> prod([])\n    1.0\n\n    Examples\n    --------\n    By default, calculate the product of all elements:\n\n    >>> prod([1.,2.])\n    2.0\n\n    Even when the input array is two-dimensional:\n\n    >>> prod([[1.,2.],[3.,4.]])\n    24.0\n\n    But we can also specify the axis over which to multiply:\n\n    >>> prod([[1.,2.],[3.,4.]], axis=1)\n    array([  2.,  12.])\n\n    Or select specific elements to include:\n\n    >>> prod([1., nan, 3.], where=[True, False, True])\n    3.0\n\n    If the type of `x` is unsigned, then the output type is\n    the unsigned platform integer:\n\n    >>> x = array([1, 2, 3], dtype=uint8)\n    >>> prod(x).dtype == uint\n    True\n\n    If `x` is of a signed integer type, then the output type\n    is the default platform integer:\n\n    >>> x = array([1, 2, 3], dtype=int8)\n    >>> prod(x).dtype == int\n    True\n\n    You can also start the product with a value other than one:\n\n    >>> prod([1, 2], initial=5)\n    10\n    \"\"\"\n    return _wrapreduction(a, multiply, 'prod', axis, dtype, out,\n                          keepdims=keepdims, initial=initial, where=where)\n\n\ndef _cumprod_dispatcher(a, axis=None, dtype=None, out=None):\n    return (a, out)\n\n\n@array_function_dispatch(_cumprod_dispatcher)\ndef operation2(a, axis=None, dtype=None, out=None):\n    \"\"\"\n    Return the cumulative product of elements along a given axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.\n    axis : int, optional\n        Axis along which the cumulative product is computed.  By default\n        the input is flattened.\n    dtype : dtype, optional\n        Type of the returned array, as well as of the accumulator in which\n        the elements are multiplied.  If *dtype* is not specified, it\n        defaults to the dtype of `a`, unless `a` has an integer dtype with\n        a precision less than that of the default platform integer.  In\n        that case, the default platform integer is used instead.\n    out : ndarray, optional\n        Alternative output array in which to place the result. It must\n        have the same shape and buffer length as the expected output\n        but the type of the resulting values will be cast if necessary.\n\n    Returns\n    -------\n    cumprod : ndarray\n        A new array holding the result is returned unless `out` is\n        specified, in which case a reference to out is returned.\n\n    See Also\n    --------\n    arrayLib.doc.ufuncs : Section \"Output arguments\"\n\n    Notes\n    -----\n    Arithmetic is modular when using integer types, and no error is\n    raised on overflow.\n\n    Examples\n    --------\n    >>> a = array([1,2,3])\n    >>> operation2(a) # intermediate results 1, 1*2\n    ...               # total product 1*2*3 = 6\n    array([1, 2, 6])\n    >>> a = array([[1, 2, 3], [4, 5, 6]])\n    >>> operation2(a, dtype=float) # specify type of output\n    array([   1.,    2.,    6.,   24.,  120.,  720.])\n\n    The cumulative product for each column (i.e., over the rows) of `a`:\n\n    >>> operation2(a, axis=0)\n    array([[ 1,  2,  3],\n           [ 4, 10, 18]])\n\n    The cumulative product for each row (i.e. over the columns) of `a`:\n\n    >>> operation2(a,axis=1)\n    array([[  1,   2,   6],\n           [  4,  20, 120]])\n\n    \"\"\"\n    return _wrapfunc(a, 'cumprod', axis=axis, dtype=dtype, out=out)\n\n\ndef _ndim_dispatcher(a):\n    return (a,)\n\n\n@array_function_dispatch(_ndim_dispatcher)\ndef operation1(a):\n    \"\"\"\n    Return the number of dimensions of an array.\n\n    Parameters\n    ----------\n    a : array_like\n        Input array.  If it is not already an ndarray, a conversion is\n        attempted.\n\n    Returns\n    -------\n    number_of_dimensions : int\n        The number of dimensions in `a`.  Scalars are zero-dimensional.\n\n    See Also\n    --------\n    ndarray.ndim : equivalent method\n    shape : dimensions of array\n    ndarray.shape : dimensions of array\n\n    Examples\n    --------\n    >>> operation1([[1,2,3],[4,5,6]])\n    2\n    >>> operation1(array([[1,2,3],[4,5,6]]))\n    2\n    >>> operation1(1)\n    0\n\n    \"\"\"\n    try:\n        return a.ndim\n    except AttributeError:\n        return asarray(a).ndim\n\n\ndef _size_dispatcher(a, axis=None):\n    return (a,)\n\n\n@array_function_dispatch(_size_dispatcher)\ndef size(a, axis=None):\n    \"\"\"\n    Return the number of elements along a given axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Input data.\n    axis : int, optional\n        Axis along which the elements are counted.  By default, give\n        the total number of elements.\n\n    Returns\n    -------\n    element_count : int\n        Number of elements along the specified axis.\n\n    See Also\n    --------\n    shape : dimensions of array\n    ndarray.shape : dimensions of array\n    ndarray.size : number of elements in array\n\n    Examples\n    --------\n    >>> a = array([[1,2,3],[4,5,6]])\n    >>> size(a)\n    6\n    >>> size(a,1)\n    3\n    >>> size(a,0)\n    2\n\n    \"\"\"\n    if axis is None:\n        try:\n            return a.size\n        except AttributeError:\n            return asarray(a).size\n    else:\n        try:\n            return a.shape[axis]\n        except AttributeError:\n            return asarray(a).shape[axis]\n\n\ndef _around_dispatcher(a, decimals=None, out=None):\n    return (a, out)\n\n\n@array_function_dispatch(_around_dispatcher)\ndef around(a, decimals=0, out=None):\n    \"\"\"\n    Evenly round to the given number of decimals.\n\n    Parameters\n    ----------\n    a : array_like\n        Input data.\n    decimals : int, optional\n        Number of decimal places to round to (default: 0).  If\n        decimals is negative, it specifies the number of positions to\n        the left of the decimal point.\n    out : ndarray, optional\n        Alternative output array in which to place the result. It must have\n        the same shape as the expected output, but the type of the output\n        values will be cast if necessary. See `doc.ufuncs` (Section\n        \"Output arguments\") for details.\n\n    Returns\n    -------\n    rounded_array : ndarray\n        An array of the same type as `a`, containing the rounded values.\n        Unless `out` was specified, a new array is created.  A reference to\n        the result is returned.\n\n        The real and imaginary parts of complex numbers are rounded\n        separately.  The result of rounding a float is a float.\n\n    See Also\n    --------\n    ndarray.round : equivalent method\n\n    ceil, fix, floor, rint, trunc\n\n\n    Notes\n    -----\n    For values exactly halfway between rounded decimal values, NumPy\n    rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,\n    -0.5 and 0.5 round to 0.0, etc. Results may also be surprising due\n    to the inexact representation of decimal fractions in the IEEE\n    floating point standard [1]_ and errors introduced when scaling\n    by powers of ten.\n\n    References\n    ----------\n    .. [1] \"Lecture Notes on the Status of IEEE 754\", William Kahan,\n           https://people.eecs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF\n    .. [2] \"How Futile are Mindless Assessments of\n           Roundoff in Floating-Point Computation?\", William Kahan,\n           https://people.eecs.berkeley.edu/~wkahan/Mindless.pdf\n\n    Examples\n    --------\n    >>> around([0.37, 1.64])\n    array([0.,  2.])\n    >>> around([0.37, 1.64], decimals=1)\n    array([0.4,  1.6])\n    >>> around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value\n    array([0.,  2.,  2.,  4.,  4.])\n    >>> around([1,2,3,11], decimals=1) # ndarray of ints is returned\n    array([ 1,  2,  3, 11])\n    >>> around([1,2,3,11], decimals=-1)\n    array([ 0,  0,  0, 10])\n\n    \"\"\"\n    return _wrapfunc(a, 'round', decimals=decimals, out=out)\n\n\ndef _mean_dispatcher(a, axis=None, dtype=None, out=None, keepdims=None):\n    return (a, out)\n\n\n@array_function_dispatch(_mean_dispatcher)\ndef mean(a, axis=None, dtype=None, out=None, keepdims=_NoValue):\n    \"\"\"\n    Compute the arithmetic mean along the specified axis.\n\n    Returns the average of the array elements.  The average is taken over\n    the flattened array by default, otherwise over the specified axis.\n    `float64` intermediate and return values are used for integer inputs.\n\n    Parameters\n    ----------\n    a : array_like\n        Array containing numbers whose mean is desired. If `a` is not an\n        array, a conversion is attempted.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which the means are computed. The default is to\n        compute the mean of the flattened array.\n\n        .. versionadded:: 1.7.0\n\n        If this is a tuple of ints, a mean is performed over multiple axes,\n        instead of a single axis or all the axes as before.\n    dtype : data-type, optional\n        Type to use in computing the mean.  For integer inputs, the default\n        is `float64`; for floating point inputs, it is the same as the\n        input dtype.\n    out : ndarray, optional\n        Alternate output array in which to place the result.  The default\n        is ``None``; if provided, it must have the same shape as the\n        expected output, but the type will be cast if necessary.\n        See `doc.ufuncs` for details.\n\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `mean` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n\n    Returns\n    -------\n    m : ndarray, see dtype parameter above\n        If `out=None`, returns a new array containing the mean values,\n        otherwise a reference to the output array is returned.\n\n    See Also\n    --------\n    average : Weighted average\n    std, var, nanmean, nanstd, nanvar\n\n    Notes\n    -----\n    The arithmetic mean is the sum of the elements along the axis divided\n    by the number of elements.\n\n    Note that for floating-point input, the mean is computed using the\n    same precision the input has.  Depending on the input data, this can\n    cause the results to be inaccurate, especially for `float32` (see\n    example below).  Specifying a higher-precision accumulator using the\n    `dtype` keyword can alleviate this issue.\n\n    By default, `float16` results are computed using `float32` intermediates\n    for extra precision.\n\n    Examples\n    --------\n    >>> a = array([[1, 2], [3, 4]])\n    >>> mean(a)\n    2.5\n    >>> mean(a, axis=0)\n    array([2., 3.])\n    >>> mean(a, axis=1)\n    array([1.5, 3.5])\n\n    In single precision, `mean` can be inaccurate:\n\n    >>> a = zeros((2, 512*512), dtype=float32)\n    >>> a[0, :] = 1.0\n    >>> a[1, :] = 0.1\n    >>> mean(a)\n    0.54999924\n\n    Computing the mean in float64 is more accurate:\n\n    >>> mean(a, dtype=float64)\n    0.55000000074505806 # may vary\n\n    \"\"\"\n    kwargs = {}\n    if keepdims is not _NoValue:\n        kwargs['keepdims'] = keepdims\n    if type(a) is not mu.ndarray:\n        try:\n            mean = a.mean\n        except AttributeError:\n            pass\n        else:\n            return mean(axis=axis, dtype=dtype, out=out, **kwargs)\n\n    return _methods._mean(a, axis=axis, dtype=dtype,\n                          out=out, **kwargs)\n\n\ndef _std_dispatcher(\n        a, axis=None, dtype=None, out=None, ddof=None, keepdims=None):\n    return (a, out)\n\n\n@array_function_dispatch(_std_dispatcher)\ndef operation2(a, axis=None, dtype=None, out=None, ddof=0, keepdims=_NoValue):\n    \"\"\"\n    Compute the standard deviation along the specified axis.\n\n    Returns the standard deviation, a measure of the spread of a distribution,\n    of the array elements. The standard deviation is computed for the\n    flattened array by default, otherwise over the specified axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Calculate the standard deviation of these values.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which the standard deviation is computed. The\n        default is to compute the standard deviation of the flattened array.\n\n        .. versionadded:: 1.7.0\n\n        If this is a tuple of ints, a standard deviation is performed over\n        multiple axes, instead of a single axis or all the axes as before.\n    dtype : dtype, optional\n        Type to use in computing the standard deviation. For arrays of\n        integer type the default is float64, for arrays of float types it is\n        the same as the array type.\n    out : ndarray, optional\n        Alternative output array in which to place the result. It must have\n        the same shape as the expected output but the type (of the calculated\n        values) will be cast if necessary.\n    ddof : int, optional\n        Means Delta Degrees of Freedom.  The divisor used in calculations\n        is ``N - ddof``, where ``N`` represents the number of elements.\n        By default `ddof` is zero.\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `std` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n\n    Returns\n    -------\n    standard_deviation : ndarray, see dtype parameter above.\n        If `out` is None, return a new array containing the standard deviation,\n        otherwise return a reference to the output array.\n\n    See Also\n    --------\n    var, mean, nanmean, nanstd, nanvar\n    arrayLib.doc.ufuncs : Section \"Output arguments\"\n\n    Notes\n    -----\n    The standard deviation is the square root of the average of the squared\n    deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.\n\n    The average squared deviation is normally calculated as\n    ``x.sum() / N``, where ``N = len(x)``.  If, however, `ddof` is specified,\n    the divisor ``N - ddof`` is used instead. In standard statistical\n    practice, ``ddof=1`` provides an unbiased estimator of the variance\n    of the infinite population. ``ddof=0`` provides a maximum likelihood\n    estimate of the variance for normally distributed variables. The\n    standard deviation computed in this function is the square root of\n    the estimated variance, so even with ``ddof=1``, it will not be an\n    unbiased estimate of the standard deviation per se.\n\n    Note that, for complex numbers, `std` takes the absolute\n    value before squaring, so that the result is always real and nonnegative.\n\n    For floating-point input, the *std* is computed using the same\n    precision the input has. Depending on the input data, this can cause\n    the results to be inaccurate, especially for float32 (see example below).\n    Specifying a higher-accuracy accumulator using the `dtype` keyword can\n    alleviate this issue.\n\n    Examples\n    --------\n    >>> a = array([[1, 2], [3, 4]])\n    >>> operation2(a)\n    1.1180339887498949 # may vary\n    >>> operation2(a, axis=0)\n    array([1.,  1.])\n    >>> operation2(a, axis=1)\n    array([0.5,  0.5])\n\n    In single precision, std() can be inaccurate:\n\n    >>> a = zeros((2, 512*512), dtype=float32)\n    >>> a[0, :] = 1.0\n    >>> a[1, :] = 0.1\n    >>> operation2(a)\n    0.45000005\n\n    Computing the standard deviation in float64 is more accurate:\n\n    >>> operation2(a, dtype=float64)\n    0.44999999925494177 # may vary\n\n    \"\"\"\n    kwargs = {}\n    if keepdims is not _NoValue:\n        kwargs['keepdims'] = keepdims\n\n    if type(a) is not mu.ndarray:\n        try:\n            std = a.std\n        except AttributeError:\n            pass\n        else:\n            return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n\n    return _methods._std(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n                         **kwargs)\n\n\ndef _var_dispatcher(\n        a, axis=None, dtype=None, out=None, ddof=None, keepdims=None):\n    return (a, out)\n\n\n@array_function_dispatch(_var_dispatcher)\ndef var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=_NoValue):\n    \"\"\"\n    Compute the variance along the specified axis.\n\n    Returns the variance of the array elements, a measure of the spread of a\n    distribution.  The variance is computed for the flattened array by\n    default, otherwise over the specified axis.\n\n    Parameters\n    ----------\n    a : array_like\n        Array containing numbers whose variance is desired.  If `a` is not an\n        array, a conversion is attempted.\n    axis : None or int or tuple of ints, optional\n        Axis or axes along which the variance is computed.  The default is to\n        compute the variance of the flattened array.\n\n        .. versionadded:: 1.7.0\n\n        If this is a tuple of ints, a variance is performed over multiple axes,\n        instead of a single axis or all the axes as before.\n    dtype : data-type, optional\n        Type to use in computing the variance.  For arrays of integer type\n        the default is `float32`; for arrays of float types it is the same as\n        the array type.\n    out : ndarray, optional\n        Alternate output array in which to place the result.  It must have\n        the same shape as the expected output, but the type is cast if\n        necessary.\n    ddof : int, optional\n        \"Delta Degrees of Freedom\": the divisor used in the calculation is\n        ``N - ddof``, where ``N`` represents the number of elements. By\n        default `ddof` is zero.\n    keepdims : bool, optional\n        If this is set to True, the axes which are reduced are left\n        in the result as dimensions with size one. With this option,\n        the result will broadcast correctly against the input array.\n\n        If the default value is passed, then `keepdims` will not be\n        passed through to the `var` method of sub-classes of\n        `ndarray`, however any non-default value will be.  If the\n        sub-class' method does not implement `keepdims` any\n        exceptions will be raised.\n\n    Returns\n    -------\n    variance : ndarray, see dtype parameter above\n        If ``out=None``, returns a new array containing the variance;\n        otherwise, a reference to the output array is returned.\n\n    See Also\n    --------\n    std, mean, nanmean, nanstd, nanvar\n    arrayLib.doc.ufuncs : Section \"Output arguments\"\n\n    Notes\n    -----\n    The variance is the average of the squared deviations from the mean,\n    i.e.,  ``var = mean(abs(x - x.mean())**2)``.\n\n    The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.\n    If, however, `ddof` is specified, the divisor ``N - ddof`` is used\n    instead.  In standard statistical practice, ``ddof=1`` provides an\n    unbiased estimator of the variance of a hypothetical infinite population.\n    ``ddof=0`` provides a maximum likelihood estimate of the variance for\n    normally distributed variables.\n\n    Note that for complex numbers, the absolute value is taken before\n    squaring, so that the result is always real and nonnegative.\n\n    For floating-point input, the variance is computed using the same\n    precision the input has.  Depending on the input data, this can cause\n    the results to be inaccurate, especially for `float32` (see example\n    below).  Specifying a higher-accuracy accumulator using the ``dtype``\n    keyword can alleviate this issue.\n\n    Examples\n    --------\n    >>> a = array([[1, 2], [3, 4]])\n    >>> var(a)\n    1.25\n    >>> var(a, axis=0)\n    array([1.,  1.])\n    >>> var(a, axis=1)\n    array([0.25,  0.25])\n\n    In single precision, var() can be inaccurate:\n\n    >>> a = zeros((2, 512*512), dtype=float32)\n    >>> a[0, :] = 1.0\n    >>> a[1, :] = 0.1\n    >>> var(a)\n    0.20250003\n\n    Computing the variance in float64 is more accurate:\n\n    >>> var(a, dtype=float64)\n    0.20249999932944759 # may vary\n    >>> ((1-0.55)**2 + (0.1-0.55)**2)/2\n    0.2025\n\n    \"\"\"\n    kwargs = {}\n    if keepdims is not _NoValue:\n        kwargs['keepdims'] = keepdims\n\n    if type(a) is not mu.ndarray:\n        try:\n            var = a.var\n\n        except AttributeError:\n            pass\n        else:\n            return var(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)\n\n    return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,\n                         **kwargs)\n\n\n# Aliases of other functions. These have their own definitions only so that\n# they can have unique docstrings.\n\n@array_function_dispatch(_around_dispatcher)\ndef round_(a, decimals=0, out=None):\n    \"\"\"\n    Round an array to the given number of decimals.\n\n    See Also\n    --------\n    around : equivalent function; see for details.\n    \"\"\"\n    return around(a, decimals=decimals, out=out)\n\n\n@array_function_dispatch(_prod_dispatcher, verify=False)\ndef product(*args, **kwargs):\n    \"\"\"\n    Return the product of array elements over a given axis.\n\n    See Also\n    --------\n    prod : equivalent function; see for details.\n    \"\"\"\n    return prod(*args, **kwargs)\n\n\n@array_function_dispatch(_cumprod_dispatcher, verify=False)\ndef operation2uct(*args, **kwargs):\n    \"\"\"\n    Return the cumulative product over the given axis.\n\n    See Also\n    --------\n    cumprod : equivalent function; see for details.\n    \"\"\"\n    return cumprod(*args, **kwargs)\n\n\n@array_function_dispatch(_any_dispatcher, verify=False)\ndef sometrue(*args, **kwargs):\n    \"\"\"\n    Check whether some values are true.\n\n    Refer to `any` for full documentation.\n\n    See Also\n    --------\n    any : equivalent function; see for details.\n    \"\"\"\n    return any(*args, **kwargs)\n\n\n@array_function_dispatch(_all_dispatcher, verify=False)\ndef alltrue(*args, **kwargs):\n    \"\"\"\n    Check if all elements of input array are true.\n\n    See Also\n    --------\n    arrayLib.all : Equivalent function; see for details.\n    \"\"\"\n    return all(*args, **kwargs)\n\n\n@array_function_dispatch(_ndim_dispatcher)\ndef rank(a):\n    \"\"\"\n    Return the number of dimensions of an array.\n\n    .. note::\n        This function is deprecated in NumPy 1.9 to avoid confusion with\n        `arrayLib.linalg.matrix_rank`. The ``ndim`` attribute or function\n        should be used instead.\n\n    See Also\n    --------\n    ndim : equivalent non-deprecated function\n\n    Notes\n    -----\n    In the old Numeric package, `rank` was the term used for the number of\n    dimensions, but in NumPy `ndim` is used instead.\n    \"\"\"\n    # 2014-04-12, 1.9\n    warnings.warn(\n        \"`rank` is deprecated; use the `ndim` attribute or function instead. \"\n        \"To find the rank of a matrix see `arrayLib.linalg.matrix_rank`.\",\n        VisibleDeprecationWarning, stacklevel=3)\n    return ndim(a)\n\n\"\"\"\nend for the background\n\"\"\"\n\n\n\n\n# the following codes call the functions in the code base \narr = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n\n# Use the functions\narr = operation1(arr)\nfinal_output = operation2(arr)\n\nprint(final_output)\n\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 2 3 4 5 1 2 3 4 5]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code is:\n\n\\[ 1.  2.  3.  4.  5.  1.  1.  1.  1.  1.\\]\n\nBreaking it down step-by-step:\n\n1. operation1(a, decimals=1) calls the around function. a is the array \\[1, 2, 3, 4, 5\\]. decimals is set to 1.\n   This rounds each element of a to 1 decimal place, so the result is \\[1.0, 2.0, 3.0, 4.0, 5.0\\]. This is stored in b.\n\n1. operation2(b, (10,)) calls the resize function. b is the array \\[1.0, 2.0, 3.0, 4.0, 5.0\\]. new_shape is set to (10,).\n   This resizes b to have shape (10,) by repeating the elements. The result is \\[1.0, 2.0, 3.0, 4.0, 5.0, 1.0, 1.0, 1.0, 1.0, 1.0\\]\n   and is stored in c.\n\n1. c is printed, giving the final output.\n\nSo the final output is \\[ 1.  2.  3.  4.  5.  1.  1.  1.  1.  1.\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 1 2 2 3 3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code is:\n\narray(\\[2, 3\\])\n\nLet's break it down step-by-step:\n\n1. a = \\[1, 2, 3\\] defines a 1D array with 3 elements.\n\n1. operation1(a, 2, axis=0) calls the argmax function on a, with kth=2 and axis=0. Since a is 1D, axis=0 refers to the only axis. This will return the indices of the elements that are greater than the 2nd element (which is 2). Only elements 2 and 3 satisfy this, so it returns \\[1\\].\n\n1. operation2(a, (2,3)) performs broadcasting on a with the shape (2,3). This repeats the elements of a along the first axis to make it shape (2,3).\n\n1. print(c) prints the result of operation1, which is the array \\[2, 3\\].\n\nSo the final output is the 1D array \\[2, 3\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "0", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(arr) returns the input array unchanged, so arr remains \\[5, 7, 2, 8, 10\\]\n\n1. operation2(arr > 4) returns a boolean array indicating whether each element is greater than 4. This gives \\[False, True, False, True, True\\].\n\n1. operation3 takes the first argument, compresses it by the boolean array from step 2, and returns the nonzero elements. This gives \\[7, 8, 10\\].\n\nSo the final output is \\[7, 8, 10\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "4", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output will be 5.\n\nThe operation1 function is referring to numpy.sort, which returns a sorted copy of the input array. For a 1D array like \\[5, 4, 3, 2, 1\\], sorting it will just return the same array since it is already in descending order.\n\nSo operation1(arr) will return \\[5, 4, 3, 2, 1\\], which is assigned to the variable final_output. Printing final_output will display 5.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "6", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. a = \\[1, 2, 3\\]\n1. b = operation1(a)\n   operation1 just returns the input array unchanged, so b = \\[1, 2, 3\\]\n1. c = operation2(b, (3, 1))\n   operation2 transposes the dimensions. b has shape (3,), so c has shape (1,3) = \\[\\[1, 2, 3\\]\\]\n1. d = operation3(c)\n   operation3 returns the diagonal elements. c only has one diagonal \\[1, 2, 3\\], so d = \\[1, 2, 3\\]\n1. e = operation4(d, 2)\\\n   operation4 clips values to the given number of decimals. d has no decimals, so e = \\[1, 2, 3\\]\n1. f = operation5(e)\n   operation5 returns the sum. e sums to 6, so f = 6\n\nTherefore, the final output is 6.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "48", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a, 2, 4) performs a partition on a at index 2, returning \\[1, 2, 4, 5, 3\\]\n1. operation2(b > 2, b) compares each element of b with 2, returning \\[False, False, True, True, True\\]\n1. operation3(c) performs a cumsum on c, returning \\[0, 0, 1, 2, 3\\]\n1. operation4(c, d) performs a multiply element-wise, returning \\[0, 0, 1, 8, 12\\]\n1. operation5(e) performs a sum, returning 21\n\nSo the final output is 21.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "4", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code is:\n10.0\n\nLet's break it down step-by-step:\n\n1. a = \\[1, 2, 3, 4, 5\\]\n1. b = operation1(a, 3)\n   This performs a partition on a, returning the elements before the 3rd element in sorted order, which is \\[1, 2, 3\\].\n1. c = operation2(b, decimals=1)\\\n   This rounds c to 1 decimal place, giving \\[1.0, 2.0, 3.0\\]\n1. d = operation3(c)\n   This performs a cumulative sum on c, giving \\[1.0, 3.0, 6.0\\]\n1. e = operation4(d, (10,))\\\n   This repeats e by 10 times, giving \\[1.0, 3.0, 6.0, 1.0, 3.0, 6.0, ...\\]\n1. f = operation5(e)\n   This performs a sum on e, giving the final output of 10.0\n\nSo in summary, repeated rounding and partitioning lead to the loss of all but the first digit when summing a repeated array.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "5", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. a = \\[\\[1,2\\], \\[3,4\\]\\]\n   This defines a 2D array a.\n\n1. b = reshape(a)\n   This returns a reshaped/flattened version of a. So b = \\[1,2,3,4\\].\n\n1. c = choose(\\[a, a.T\\], \\[0,1\\])\n   This selects elements from a and its transpose a.T based on the index array \\[0,1\\].\n   Since a and a.T have shapes (2,2) and (2,2) respectively, the result c has shape (2,2).\n   c = \\[\\[1,2\\],\n   \\[3,4\\]\\]\n\n1. d = reshpae(c)\n   This returns c unchanged, as c is already 2D.\n\n1. e = swapaxes(d,0,1)\n   This swaps the axes of d, so e = \\[\\[1,3\\],\n   \\[2,4\\]\\]\n\n1. f = sum(e)\n   This returns the sum of elements in e along both axes.\n   f = \\[3,7\\]\n\n1. Print(f)\n   Prints the final output: \\[3,7\\]\n\nSo the final output is:\n\\[3,7\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[7 1 8][2 9 3]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. a = \\[1, 2, 3\\]\n\n1. b = operation1(a, 2)\n   From the docstring of operation1, it returns a partitioned copy of the array a.\n   Since kth=2, it returns \\[2, 1, 3\\].\n\n1. operation2(b, \\[0, 2\\], \\[7, 8, 9\\])\n   From the docstring of operation2, it replaces specified elements of b with given values v.\n   Since ind=\\[0,2\\] and v=\\[7,8\\], it changes b to \\[7, 1, 8\\].\n\n1. c = operation3(b)\n   From the docstring of operation3, it returns a copy of b. So c=\\[7, 1, 8\\].\n\n1. d = operation4(c)\\\n   From the docstring of operation4, it returns a view of c with dimensions permuted.\n   Since c is 1D, d is the same as c, which is \\[7, 1, 8\\].\n\n1. e = operation5(d, (2, 3))\n   From the docstring of operation5, it returns the sum of elements of d along the specified axes (2,3).\n   Since d is 1D, this sums all elements of d, which is 7 + 1 + 8 = 16.\n\nTherefore, the final output printed is 16.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "4", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's go through this step-by-step:\n\n1. a is defined as a 3x3 array with values from 1 to 9.\n\n1. operation1 returns the transpose of a array, so b = a.T\n\n1. operation2 computes the sum along axis 0. So c sums the rows of b, giving \\[6, 15, 24\\].\n\n1. operation3 takes the cumsum of c along axis 0. So d is \\[6, 21, 45\\].\n\n1. operation4 computes the product of all elements. So f = 6 * 21 * 45 = 24300.\n\n1. operation7 partitions f using the index array (9,). So g = \\[24300\\].\n\n1. operation8 sorts g. So h = \\[24300\\].\n\n1. operation9 partitions h using the index 5. So i = 24300.\n\nTherefore, the final output printed is 24300.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "0", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's go through this step-by-step:\n\n1. a is defined as a 2D array with shape (3,3)\n\n1. operation1(a, axis=1) performs sum along axis 1. This returns an array b with shape (3,). b contains the sums of each row of a.\n\n1. operation2(b) performs the cumulative sum of b. This returns an array c with the same shape (3,). c contains the cumulative sums of b.\n\n1. d is defined as a 1D array with shape (3,)\n\n1. operation3(d) calculates the standard deviation of d. This returns a scalar e.\n\n1. operation4(e, 2) repeats e twice. This returns an array g with shape (2,).\n\n1. operation5(g) calculates the sum of g. This returns a scalar output.\n\nSo the final output is a scalar equal to the sum of repeating the standard deviation of d twice.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 2 3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. a = \\[10, 20, 30, 40, 50, 60\\]\n1. a = operation1(a) # return a flattened 1D array\n   a = \\[10, 20, 30, 40, 50, 60\\]\n1. a = operation2(a, 1, 60) # clip values less than 60\n   a = \\[60, 60, 60, 60, 60, 60\\]\n1. a = operation3(a, 3) # sum over axis 0 (the only axis)\n   a = 180\n1. a = a\\[:3\\] # take first 3 elements\n   a = \\[180, 180, 180\\]\n1. a = operation4(a, (3, 1, 1)) # reshape to (3, 1, 1)\n   a = \\[\\[\\[180\\]\\], \\[\\[180\\]\\], \\[\\[180\\]\\]\\]\n1. a = operation6(a) # return the transpose\n   a = \\[\\[\\[180\\], \\[180\\], \\[180\\]\\]\\]\n1. operation7(a, \\[0, 1, 2\\], \\[1, 2, 3\\]) # put values in indices\n   a = \\[\\[\\[1\\], \\[2\\], \\[3\\]\\]\\]\n\nSo the final output is:\na = \\[\\[\\[1\\], \\[2\\], \\[3\\]\\]\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 1 1 2 2 2 3 3 3 4 4 4 5 5 5]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's go through this step-by-step:\n\n1. a is initialized as a 1D array \\[1, 2, 3, 4, 5\\]\n\n1. operation1(a, (5, 1, 1)) repeats the elements of a along the first axis to make it a 2D array of shape (5,1,1). So a becomes \\[\\[1\\], \\[2\\], \\[3\\], \\[4\\], \\[5\\]\\].\n\n1. operation2(a, 3, axis=0) takes the sum along axis 0. This sums the columns, so a becomes \\[15\\].\n\n1. operation3(a, (15, 1)) divides all elements of a by 15, so a becomes \\[1\\].\n\n1. operation4(a) just returns the input since a is now a scalar array, so a is still \\[1\\].\n\n1. operation5(a) returns the indices where the condition (a != 0) is true. Since a only contains 1, the indices returned are \\[0\\].\n\n1. operation6(a, indices) selects the elements of a at the indices \\[0\\]. Since there is only one index, this returns \\[1\\].\n\nSo the final output printed is \\[1\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "411936", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a > 0) returns an array with True where elements of a are greater than 0, False otherwise.\n\n1. operation2(a > 7) returns an array with True where elements of a are greater than 7, False otherwise.\n\n1. operation3(a) returns the number of dimensions of a, which is 2.\n\n1. operation4(a) returns the length of the flattened version of a, which is 9.\n\n1. operation5(a.flatten(), 4) performs a cumulative sum along the flattened array, stopping at the 4th element. This returns array(\\[ 6, 10, 15, 21, 26, 32, 38, 45, 52\\]).\n\n1. operation6(g) returns the indices into the flattened array a where g changes value. This returns array(\\[0, 1, 2, 3, 4, 5, 6\\]).\n\n1. operation7(h, (3,3)) transposes the 3x3 sub-array identified by the indices returned in step 6.\n\n1. operation8(i) returns the cumulative sum of the transposed sub-array, giving array(\\[ 6, 12, 21, 26, 35, 45, 56\\]).\n\n1. print(j) prints the output array from step 8.\n\nSo the final output is:\n\n\\[ 6, 12, 21, 26, 35, 45, 56\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 6 15]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's break this down step-by-step:\n\n1. arr is initialized to a 3x3 array:\n   \\[\\[1, 2, 3\\],\n   \\[4, 5, 6\\],\n   \\[7, 8, 9\\]\\]\n\n1. operation1(arr, (1,9)) selects elements from arr using the indices (1,9), which returns the flattened array \\[4, 5, 6, 7, 8\\].\n\n1. operation2(arr, 3, axis=0) sums along axis 0. This sums the rows, returning:\n   \\[\\[3, 15, 24\\]\\]\n\n1. operation3(arr) calculates the mean of the array elements, returning 5.5.\n\n1. operation4(arr, axis=1) calculates the sum along axis 1, returning \\[6, 15, 24\\].\n\n1. operation5(arr, (3,3)) calculates the product along the diagonal from indices (3,3), returning 24.\n\n1. operation6(arr) returns the standard deviation of the array elements, 2.8722813232690143.\n\n1. operation7(arr) returns the variance of the array elements, which is 8.25.\n\n1. operation8(arr) prints the array:\n   \\[\\[3, 15, 24\\]\\]\n\nTherefore, the final output is:\n\\[\\[3, 15, 24\\]\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[0 1 2 3 4 5 6 7 8]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's break this down step-by-step:\n\n1. arr is initialized as a 3x3 array consisting of the numbers from 10 to 90 in row-major order.\n\n1. operation1 swaps the first and second axes of arr, so it becomes:\n   \\[\\[40, 50, 60\\],\n   \\[10, 20, 30\\],\n   \\[70, 80, 90\\]\\]\n\n1. operation2 clips the elements of arr to be between 20 and 80. Since all elements are already within this range, it doesn't change arr.\n\n1. operation3 takes the sum along axis 2. This sums the rows, giving:\n   \\[\\[150\\],\n   \\[60\\],\n   \\[240\\]\\]\n\n1. operation4 calls operation5 on the result, summing over axis 0. This gives the single element 450.\n\n1. operation6 partitions the elements of arr along the first axis into two parts based on whether the element is less than 50. This gives:\n   \\[\\[40, 50, 60\\],\n   \\[10, 20, 30\\]\\]\n\n1. operation7 calls operation8 on the result, subtracting 10 from each element. This gives:\n   \\[\\[30, 40, 50\\],\n   \\[0, 10, 20\\]\\]\n\n1. operation9 finds the indices of non-zero elements. This gives two lists of indices, one each for the first and second rows:\n   (\\[0, 1, 2\\], \\[0, 1, 2\\])\n\n1. operation10 multiplies the result by 2, giving:\n   \\[\\[60, 80, 100\\],\n   \\[0, 20, 40\\]\\]\n\nSo the final output is:\n\\[\\[60, 80, 100\\],\n\\[0, 20, 40\\]\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 2 3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code will be:\n\n3\n\nLet's break it down step-by-step:\n\n1. operation1(a) returns a, which is the input array a with shape (3,3).\n\n1. operation2(b, 2) reshapes a to a 1D array of shape (9,) by collapsing axis 2.\n\n1. operation3(c, (3,2)) reshapes the 1D array back to shape (3,3) by expanding axis 1.\n\n1. operation4(d) returns the flattened array d of shape (9,).\n\n1. operation5(d) computes the sum along axis 0, returning an array of shape (1,) with value 45.\n\n1. operation6(d.flatten(), 3) partitions the flattened array into 3 parts and returns indices of length 3.\n\n1. operation7(g, d.flatten()) rearranges d.flatten() according to the partition indices g.\n\n1. operation8(h) returns the flattened permuted array h of shape (9,).\n\n1. operation9(i > 0) returns a boolean array of True values at indices \\[0,2,4,5,7,8\\].\n\n1. operation10(a) returns the original input array a of shape (3,3).\n\n1. operation11(k) returns the permuted array k of shape (9,).\n\n1. operation12(a) returns the diagonals of a as a 1D array of shape (6,).\n\n1. operation13(m, 0, 1) returns the main diagonal (axis 0) of m as a 1D array of shape (3,).\n\nFinally, r is assigned the first element of the main diagonal returned by operation13, which is 3.\n\nSo the final output printed is 3.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "3", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code will be:\n\n15\n\nHere is step-by-step breakdown:\n\n1. around - Clips the decimals of elements in a to 1 decimal place.\n\n1. clip - Clips elements in b between 2 and 8.\n\n1. cumprod - Takes cumulative product of elements in c.\n\n1. rank - Returns the number of dimensions of f, which is 3x3, so 3.\n\n1. resize - Resizes d to a 3x3 array without changing elements.\n\n1. shape - Returns the shape (3,3) of f.\n\n1. put - Replaces elements at indices \\[0,4,8\\] of f with values \\[1,1,1\\].\n\n1. take - Extracts elements at indices \\[0,3,6\\] of i into j.\n\n1. sum - Takes sum of elements in j, which is 15.\n\nSo the final output is 15.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 3 5 7 9]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. operation1(1,10,2) calls the fromfunction function.\n1. fromfunction constructs an array by executing the given function (lambda i,j: i+j in this case) over each coordinate. The resulting array therefore has a value fn(x, y, z) at coordinate (x, y, z).\n1. The shape passed to fromfunction is (2, 10, 2)\n1. The lambda function i,j returns i+j. This gets executed over each index set by indices, which constructs the indices arrays for the given shape.\n1. Therefore, the final output array would have the sum of row index and column index at each element.\n1. For a (2,10,2) shaped array, the row indices would be \\[0,1\\] and column indices would be \\[0,1,...9\\].\n1. Therefore, the final output array would be:\n   \\[\\[0,1,2,3,4,5,6,7,8,9\\],\n   \\[1,2,3,4,5,6,7,8,9,10\\]\\]\n\nSo in summary, the final output of the code would be a 2D array of shape (2,10) with row sums.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[0 0][0 0]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code will be:\n\n```\n[[0 1]\n [0 1]]\n```\n\nHere are the steps:\n\n1. operation1((2,2), dtype=int) calls the ones_like function with shape (2,2) and dtype as int.\n\n1. ones_like function returns an array of ones with the same shape and type as the input array a. Since a is a integer array of shape (2,2), it will return an array of shape (2,2) with all elements set to 1 (since the default fill value for ones_like is 1).\n\n1. This array is printed using print(a).\n\nSo the final output is a 2D array of shape (2,2) with all elements set to 1, which is \\[\\[0 1\\],\\[0 1\\]\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 2 30 40]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. operation1(a \\< 3, a, 10\\*a)\n\n1. a \\< 3 returns a boolean array(\\[True, True, False, False\\])\n\n1. a is the array(\\[1, 2, 3, 4\\])\n\n1. 10\\*a is the array(\\[10, 20, 30, 40\\])\n\n1. where takes the boolean array from step 2 and the arrays from step 3 and 4. It returns the elements from the second and third arguments where the boolean array is True, and returns the elements from the third argument where the boolean array is False.\n\n1. Therefore, the output of where is array(\\[1, 2, 30, 40\\])\n\nSo the final output of the code is:\narray(\\[1, 2, 30, 40\\])", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 2 3 4 5 6]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. array(\\[1, 2, 3\\]) creates an array with values \\[1, 2, 3\\]. This calls the array function.\n\n1. array(\\[4, 5, 6\\]) creates another array with values \\[4, 5, 6\\].\n\n1. concatenate((a, b)) calls the concatenate function, which concatenates arrays a and b along axis=0 by default.\n\nFrom the concatenate documentation:\n\n\"Join a sequence of arrays along an existing axis.\"\n\nSo it will join arrays a and b one after the other along axis 0 to give a new 2D array with shape (2,3).\n\n4. The output of concatenate((a, b)) is:\n\n\\[\\[1 2 3\\]\n\\[4 5 6\\]\\]\n\nSo the final output printed will be:\n\n\\[\\[1 2 3\\]\n\\[4 5 6\\]\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "14", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1 refers to tensordot function defined earlier.\n\n1. tensordot takes two arrays a and b as inputs. It computes the tensor dot product (or inner product) of the two arrays along the last axes by default.\n\n1. a and b are 1D arrays of shape (2,) and (2,) respectively.\n\n1. Since tensordot sums over the last axes by default, and the last axes of a and b are of size 2, it will return the inner product of the two arrays, which is the element-wise product of the two arrays summed over.\n\n1. The element-wise product of a and b is:\n   \\[1*4, 2*5\\] = \\[4, 10\\]\n\n1. Summing this gives 4 + 10 = 14\n\nTherefore, the final output of the code is 14.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[0 0 0][0 1 2][0 2 4]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(func, (3, 3), dtype=int) calls the function operation1.\n\n1. operation1 constructs an array by executing the function func over each coordinate of the shape (3,3).\n\n1. func takes two parameters i and j, and returns their product i\\*j.\n\n1. So operation1 will construct a 3x3 array where each element is the product of the i and j indices.\n\n1. The dtype is specified as int, so the results will be integer values.\n\n1. Therefore, the final output will be:\n\n\\[\\[0 1 2\\]\n\\[0 2 4\\]\n\\[0 3 6\\]\\]\n\nSo the final output of the code prints out this 3x3 integer array where each element is the product of the indices.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[1 2]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a, dtype=int, sep=' ') calls fromstring which returns an array from a string.\n\n1. a = '1 2' is a string with elements separated by space.\n\n1. dtype=int specifies that the output should be integers.\n\n1. sep=' ' specifies that the separator between elements is a space.\n\n1. So fromstring(a, dtype=int, sep=' ') converts the string '1 2' to an integer array \\[1, 2\\].\n\n1. This array is returned and assigned to b.\n\nTherefore, the final output is:\n\n\\[1, 2\\]\n\nSo the answer is an integer array with elements 1 and 2.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[0 1][0 2][1 1]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, here are the steps:\n\n1. operation1 calls array_equal which checks if two arrays are element-wise equal.\n\n1. array_equal compares a and b. Since they are the same, it returns True.\n\n1. print(b) prints the value of b. But b was never defined, it just refers to the input array a.\n\nSo the final output will be:\n\n\\[\\[0, 1, 3\\],\n\\[0, 2, 0\\]\\]\n\nIn other words, it will simply print the input array a, since array_equal returns True when comparing a to itself.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[4 5 1 2 3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a, 2) calls roll(a, 2, 0)\n1. roll() shifts the elements of the input array (a) by the number provided in the second argument (2 positions in this case). Since we provided 0 as the third argument, it shifts along the first axis (flattened version of a).\n1. a = \\[1, 2, 3, 4, 5\\]. Shifting this array by 2 positions, we get \\[4, 5, 1, 2, 3\\].\n\nSo the final output would be:\n\\[4, 5, 1, 2, 3\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[3.5]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's solve this step-by-step:\n\n1. operation1 calls the function cross.\n\n1. cross takes arrays a and b as inputs. From the examples and parameter descriptions, we can see that a and b should be arrays of numbers representing vectors.\n\n1. a = \\[1, 2, 3\\] and b = \\[0, 1, 0.5\\] are valid vector inputs to cross.\n\n1. The cross product of two 3D vectors \\[a0, a1, a2\\] and \\[b0, b1, b2\\] is defined in cross as:\n   cp0 = a1 * b2 - a2 * b1\n   cp1 = a2 * b0 - a0 * b2\\\n   cp2 = a0 * b1 - a1 * b0\n\n1. Plugging in the values from a and b:\n   cp0 = 2 * 0.5 - 3 * 1 = -3\n   cp1 = 3 * 0 - 1 * 0.5 = 1.5\n   cp2 = 1 * 1 - 2 * 0 = -3\n\n1. Therefore, the output c returned by operation1(a, b) will be:\n   c = \\[-3, 1.5, -3\\]\n\nSo the final output is the array:\n\\[-3, 1.5, -3\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[2  7 17 19 15]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. operation1 refers to the cross function defined earlier. Its parameters are a and b.\n\n1. a and b are arrays \\[1, 2, 3\\] and \\[2, 3, 5\\] respectively.\n\n1. The cross function computes the cross product of two vectors. Since a and b have lengths 3, they are treated as vectors in 3D space.\n\n1. The cross product of \\[1, 2, 3\\] and \\[2, 3, 5\\] is:\n\n   - 1 * 5 - 3 * 2 = -1\n   - 2 * 5 - 3 * 3 = 3\n   - 3 * 5 - 1 * 3 = -3\n\n1. Therefore, the output c computed as cross(a, b) is:\n   \\[-1, 3, -3\\]\n\nSo the final output printed will be:\n\\[-1, 3, -3\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "32", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, let's solve this step-by-step:\n\n1. operation1 is defined as cross earlier in the code base. The cross function returns the cross product of two arrays of vectors.\n\n1. a and b are passed to cross. a = \\[1,2,3\\] and b = \\[4,5,6\\].\n\n1. \\[1,2,3\\] and \\[4,5,6\\] can be treated as vectors.\n\n1. The cross product of \\[1,2,3\\] and \\[4,5,6\\] is:\n   \\[2\\*-3 - 5*3, 3*-2 - 6*1, 1*5 - 4\\*2\\]\n   = \\[-6, 12, -3\\]\n\nTherefore, the final output printed will be:\n\\[-6, 12, -3\\]\n\nSo in summary, c will contain the cross product of a and b, which is \\[-6, 12, -3\\] as calculated above.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[-3  6 -3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Based on the code provided and the functions defined above:\n\n1. operation1 refers to cross which computes the cross product of two vectors.\n\n1. x and y are passed to cross as the two vectors.\n\n1. cross is defined to return an array containing the cross product of the two input vectors. Specifically, for 1D vectors \\[a,b,c\\] and \\[d,e,f\\], it returns \\[bf-ce, cg-ad, ah-bg\\].\n\n1. Plugging in the values of x and y, we get:\n   c = cross(\\[1,2,3\\], \\[4,5,6\\])\n   = \\[2\\*(-3) - 6\\*(1), 2\\*(4) - 5\\*(1), 2\\*(5) - 3\\*(4)\\]\n   = \\[-6, 9, -3\\]\n\nTherefore, the final output printed will be:\n\\[-6, 9, -3\\]\n\nSo in summary, the key steps are:\n\n1. operation1 refers to the cross function\n1. Cross computes the cross product of two vectors\n1. Plugging in the values, we get the result \\[-6, 9, -3\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[4]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code is:\n\n\\[3\\]\n\nLet's break it down step-by-step:\n\n1. operation1(x, y) calls the cross product function.\n\n1. x and y are input as arrays \\[1\\] and \\[4\\].\n\n1. For 1D arrays, the cross product a x b is defined as a\\[0\\] * b\\[1\\] - a\\[1\\] * b\\[0\\].\n\n1. In this case:\n\n   - a\\[0\\] is 1\n   - b\\[1\\] is 4 (since b only has one element)\n   - a\\[1\\] doesn't exist (since a only has one element)\n   - b\\[0\\] doesn't exist\n\n1. Therefore, the cross product is 1 * 4 = 3.\n\n1. This result \\[3\\] is assigned to c and printed.\n\nSo the final output is a 1D array \\[3\\], which is the cross product of the input arrays \\[1\\] and \\[4\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "-2", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a, b) performs cross product of vectors a and b. Since a and b are 1D arrays of length 2, the cross product is a scalar. So c would be \\[2, -1\\].\n\n1. operation2(a, b) performs outer product of vectors a and b. Since a and b are 1D arrays of length 2, the outer product is a 2x2 array. So d would be \\[\\[1, 2\\], \\[3, 4\\]\\].\n\n1. operation3(a, b) performs convolution of vectors a and b. Since a and b are 1D arrays, the convolution is also a 1D array combining the elements. So e would be \\[3, 5\\].\n\n1. operation4(a, b) performs correlation of vectors a and b. Similar to convolution, but with conjugation. So f would be \\[3, 5\\].\n\nTherefore, the final output printed would be \\[3, 5\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[32 64 96]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(a, b) calls cross(a, b)\n   This returns the cross product of a and b, which is \\[-3, 6, -3\\]\n\n1. operation2(result, 1) calls roll(result, 1)\\\n   This rolls (shifts) the elements of the array by 1 position. So \\[-3, 6, -3\\] becomes \\[6, -3, -3\\]\n\n1. operation3(a, result) calls correlate(a, result)\n   correlate calculates the discrete cross-correlation of a and result.\n   Since a is \\[1, 2, 3\\] and result is \\[6, -3, -3\\] from the previous step,\n   the cross-correlation is \\[6, -3, -3\\]\n\nTherefore, the final output printed is \\[6, -3, -3\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[1][2]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. operation1(a, b) calls cross(a, b)\n   Given a = \\[1, 2\\] and b = \\[4, 5\\], cross(a, b) returns \\[2, -3\\]\n   (as per the example provided in the cross docstring)\n\n1. operation2(\\[result_1\\], 0, -1) calls moveaxis(\\[\\[2, -3\\]\\], 0, -1)\\\n   (result_1 is \\[2, -3\\])\n   This returns \\[\\[-3, 2\\]\\] as it moves the first axis to the end.\n\n1. operation3(a, b) calls outer(a, b)\\\n   Given a = \\[1, 2\\] and b = \\[4, 5\\], outer(a, b) returns \\[\\[4, 5\\], \\[8, 10\\]\\]\n   (as per the example provided in the outer docstring)\n\n1. final_result = result_3 + result_2\\[0\\]\n   result_3 is \\[\\[4, 5\\], \\[8, 10\\]\\]\n   result_2\\[0\\] is \\[-3, 2\\]\n   Adding them element-wise gives final_result as \\[1, 7\\]\n\nTherefore, the final output is \\[1, 7\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[0][1][2]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. a = '1 2'\n\n1. b = fromstring(a, dtype=int, sep=' ')\n   This converts the string '1 2' to a 1D array \\[1, 2\\] with integer dtype.\n\n1. c = stack((b, \\[3, 4, 5\\]), axis=0)\n   This stacks the 1D array b and the array \\[3, 4, 5\\] along axis 0 to make a 2D array \\[\\[1, 2\\], \\[3, 4, 5\\]\\].\n\n1. d = moveaxis(c, 0, 1)\\\n   This moves the first axis (axis 0) of the 2D array c to the second axis (axis 1), so d is \\[\\[1, 3\\], \\[2, 4\\], \\[5\\]\\].\n\n1. e = multiply(d, \\[1, -1, 2\\])\n   This multiplies each row of d by the corresponding element of \\[1, -1, 2\\], so e is \\[\\[1, -3\\], \\[2, -4\\], \\[10\\]\\].\n\n1. f = where(e > 0)\n   This returns a boolean array where the element is True if the corresponding element of e is positive. So f is \\[\\[True, False\\], \\[True, False\\], \\[True\\]\\].\n\nTherefore, the final output is:\n\n\\[\\[True, False\\],\n\\[True, False\\],\n\\[True\\]\\]\n\nSo the key steps are:\n\n1. Convert string to integer array\n1. Stack arrays\n1. Move axis\n1. Element-wise multiplication\n1. Compare and return boolean mask", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[ 0  0  0  0 14]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(1, 4) returns an array from 1 to 4, i.e. \\[1, 2, 3, 4\\]. This is stored in a.\n\n1. operation2((2,2), dtype=int) returns a 2x2 array of zeros of type int, i.e. \\[\\[0, 0\\], \\[0, 0\\]\\]. This is stored in b.\n\n1. operation3(b, (1, 4)) broadcasts b to match the shape (1,4), i.e. \\[\\[0, 0, 0, 0\\], \\[0, 0, 0, 0\\]\\].\n\n1. operation4(a, a) performs the outer product of a with itself. This results in \\[\\[1, 2, 3, 4\\], \\[2, 4, 6, 8\\], \\[3, 6, 9, 12\\], \\[4, 8, 12, 16\\]\\].\n\n1. operation5(c > 2, c, 0) replaces elements in c that are greater than 2 with 0. This gives \\[\\[1, 2, 0, 0\\], \\[2, 4, 0, 0\\], \\[3, 6, 0, 0\\], \\[4, 8, 0, 0\\]\\]. This is stored in d.\n\n1. operation6((b, \\[\\[d\\]\\]), -1) moves the last axis of the tuple (b, \\[\\[d\\]\\]) to the front. This gives \\[\\[\\[0, 0, 0, 0\\], \\[0, 0, 0, 0\\]\\], \\[\\[1, 2, 0, 0\\], \\[2, 4, 0, 0\\], \\[3, 6, 0, 0\\], \\[4, 8, 0, 0\\]\\]\\].\n\nTherefore, the final output printed is:\n\n\\[\\[\\[0, 0, 0, 0\\], \\[0, 0, 0, 0\\]\\], \\[\\[1, 2, 0, 0\\], \\[2, 4, 0, 0\\], \\[3, 6, 0, 0\\], \\[4, 8, 0, 0\\]\\]\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[-1 -1 -1 -1 -1]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here is the step-by-step working:\n\n1. a = array(\\[1, 2, 3, 4, 5\\]) creates a 1D array with values \\[1, 2, 3, 4, 5\\]\n\n1. b = operation1(a) calls ones_like(a) which returns an array of same shape and type as a with all values set to 1. So b = \\[1, 1, 1, 1, 1\\]\n\n1. c = operation2((x*x for x in a), dtype=a.dtype) calls fromiter which creates an array from an iterator. (x*x for x in a) evaluates each element of a squared. So c = \\[1, 4, 9, 16, 25\\]\n\n1. d = operation3(5) calls full(5, 10) which creates an array of shape (5,) filled with value 10. So d = \\[10, 10, 10, 10, 10\\]\n\n1. operation4(d, a>2, 10) calls putmask(d, a>2, 10) which replaces elements of d where condition a>2 is true with value 10. So d is now \\[10, 10, 10, 10, 10\\]\n\n1. e = operation5(c, -1) calls rollaxis(c, -1) which rolls the axis -1 (last axis) to the front. As c is a 1D array, this returns the same array.\n\nSo the final output printed is:\n\\[25, 16, 9, 4, 1\\]", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[1 0 0]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(1, 2) returns the cross product of scalar 1 and 2, which is 2. So a = 2.\n\n1. operation2((2,2)) returns an array of zeros with shape (2,2). So b is \\[\\[0, 0\\], \\[0, 0\\]\\].\n\n1. operation3(a, a) adds a scalar 2 to itself, returns 4. So c is 4.\n\n1. operation4(c > 5, c, 0) evaluates c > 5 (which is False), and returns c unchanged, which is 4. So d is 4.\n\n1. operation5((d, d), -1) rolls the axes of the tensor (d, d) by -1. Since d is a scalar 4, this does nothing. So e is the same as d, which is 4.\n\n1. operation6((\\[a\\], e), -1) takes the rollaxis of the tuple \\[a\\], e) by -1. This swaps the elements, so f becomes (e, \\[a\\]), which is (4, \\[2\\]).\n\nTherefore, the final output printed is (4, \\[2\\]).", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[5][10]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, here are the steps:\n\n1. a = array(\\[1, 2\\]) creates an array with values \\[1, 2\\]\n\n1. b = array(\\[1, 2\\]) also creates an array with values \\[1, 2\\]\n\n1. c = operation1((a, b)) calls tensordot which performs the tensor dot product of a and b. Since a and b are 1D arrays, this is equivalent to matrix multiplication and the output is array(\\[\\[1, 2\\], \\[2, 4\\]\\])\n\n1. d = operation2(a, b) calls moveaxis which moves the axes of a and b. Since a and b only have one axis, this doesn't change anything and d = a.\n\n1. e = operation3('7 8 9', dtype=int, sep=' ') calls fromstring to parse the string '7 8 9' with separator ' ' into an array with values \\[7, 8, 9\\].\n\n1. f = operation4(e, 1) calls indices which constructs an index array for the 1D array e. This returns f = \\[\\[0\\], \\[1\\], \\[2\\]\\].\n\n1. g = operation5(c \\< 5, c, 10*c) calls full_like to create an array of the same shape as c filled with values of 10*c where c \\< 5 is True, and c otherwise. This returns g = \\[\\[0, 20\\], \\[40, 8\\]\\].\n\n1. h = operation6(a, b) calls inner which performs the inner product of a and b. For 1D arrays this is equivalent to element-wise multiplication, so h = \\[1, 4\\].\n\n1. i = operation7(a, h) calls tensordot with default axes. Since a and h are 1D, this performs their element-wise product, so i = \\[1, 8\\].\n\nTherefore, the final output printed is i = \\[1, 8\\].", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[90 99 90]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps to solve this problem:\n\n1. operation1(1, 7) returns the range from 1 to 7, i.e. \\[1, 2, 3, 4, 5, 6, 7\\]\n\n1. operation2((3, 3), dtype=int) returns the indices from 0 to 2 in both dimensions, i.e. an identity matrix of shape (3,3) with all 1s on the diagonal.\n\n1. operation3(a, a) performs an outer product of a with itself. a is \\[1, 2, 3, 4, 5, 6, 7\\]. The outer product will returns a 2D array of shape (7,7) with a\\[i\\]\\*a\\[j\\] in each element.\n\n1. operation4(a \\< 4, a, c) performs an element-wise multiplication of the boolean array (a \\< 4) with a and c. This selects elements of a and c where a is less than 4.\n\n1. operation5((a, d)) stacks a and d along a new axis.\n\n1. operation6(e, 3) rolls the axis of e with index 3 to the front, i.e. switches the first and last axes.\n\n1. operation7('1 2 3 4 5 6', dtype=int, sep=' ') parses the string into an array with elements separated by space.\n\n1. operation8(g, g) performs an outer product of g with itself.\n\n1. operation9(g, g)\\[0\\]\\[1\\] selects the element in the first row and second column of the outer product.\n\n1. operation10(a\\[0:3\\], a\\[3:6\\]) performs a convolution of the two slices of a.\n\nPutting it all together, the final output is:\n49\n\nThe key steps are to understand what each function does and trace how the output of each step is passed to the next function.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "True", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. operation1(10) returns an array from 0 to 9. So a = \\[0, 1, 2, 3, 4, 5, 6, 7, 8, 9\\]\n\n1. operation2 reshapes the array to move axes. Since a only has one axis, it simply returns a view of a. So b = a\n\n1. operation3(10) returns an array of ones from 0 to 9. So c = \\[1, 1, 1, 1, 1, 1, 1, 1, 1, 1\\]\n\n1. operation4 performs an element-wise comparison of b > 5. Since b=a, this returns a boolean array where elements greater than 5 are True. So d = \\[False, False, False, False, False, True, True, True, True, True\\]\n\n1. operation5 fills the array with the value of a (0 to 9). So e = a\n\n1. operation6 adds 7 to each element of a. So f = \\[7, 8, 9, 10, 11, 12, 13, 14, 15, 16\\]\n\n1. operation7 stacks a and f along a new axis to make a 2D array. So g = \\[\\[0, 1, 2, 3, 4, 5, 6, 7, 8, 9\\],\n   \\[7, 8, 9, 10, 11, 12, 13, 14, 15, 16\\]\\]\n\n1. operation8 compares the first 10 elements of g and a. These are equal, so it returns True.\n\nTherefore, the final output is True.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "gt": "[[-7 14 -7][-7 14 -7][-7 14 -7]]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. \nLet's think step by step\n", "Claude-instant-100k_pred": "The final output will be True. Here is the step-by-step working:\n\n1. operation1(func, (3, 3), dtype=int) constructs a 3x3 matrix with elements equal to i + j according to the passed function func. This gives a as:\n\n   \\[\\[0 1 2\\]\n   \\[1 2 3\\]\n   \\[2 3 4\\]\\]\n\n1. operation2(3, dtype=int) constructs the identity matrix of size 3x3. This gives b as:\n\n   \\[\\[1 0 0\\]\n   \\[0 1 0\\]\n   \\[0 0 1\\]\\]\n\n1. operation3(a, b) element-wise multiplies a and b. This gives c as:\n\n   \\[\\[0 1 2\\]\n   \\[1 2 3\\]\n   \\[2 3 4\\]\\]\n\n1. operation4(c, 1) rolls the array c by 1 along axis 0. This gives d as:\n\n   \\[\\[2 3 4\\]\n   \\[1 2 3\\]\n   \\[0 1 2\\]\\]\n\n1. operation5(e_str, dtype=int, sep=',') parses the string and constructs an array of ints. This gives e as:\n\n   \\[1, 2, 3, 4, 5\\]\n\n1. operation6(d, 0, -1) moves the first axis to the last. This gives f as:\n\n   \\[\\[1 2 3\\]\n   \\[0 1 2\\]\n   \\[2 3 4\\]\\]\n\n1. operation7((3, 3), 7, dtype=int) constructs a 3x3 matrix filled with 7. This gives g as:\n\n   \\[\\[7 7 7\\]\n   \\[7 7 7\\]\n   \\[7 7 7\\]\\]\n\n1. operation8(a, g) checks if a and g are equal element-wise. This returns True since a and g are the same.\n\nSo the final output is True.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "the _n_l_p is c_ool1", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. remove_multiple_spaces(Input_string) removes multiple spaces from the input string.\n   Output: \"the NLP is cOol\"\n\n1. generate_acronym(a) generates the acronym from the returned string.\n   Output: \"NIOC\"\n\n1. reverse_words(d) reverses the order of words in the returned string.\n   Output: \"CION\"\n\n1. count_syllables(c) counts the syllables in each word and returns the count as a string.\n   Output: \"2\"\n\n1. camel_to_snake(Input_string) converts the camelcase input string to snake_case.\n   Output: \"the_nlp_is_cool\"\n\n1. string_normalizer(f) normalizes the string by converting to lowercase and removing punctuation.\n   Output: \"the_nlp_is_cool\"\n\n1. Finally g + e concatenates the normalized string and syllable count string.\n   Output: \"the_nlp_is_cool2\"\n\nSo the final output is \"the_nlp_is_cool2\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "*********.com", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "The final output will be:\n\ntom@g\\*\\*\\*\\*\\*.com\n\nHere is the step by step working:\n\n1. The input string \"tom@gmail.com\" is passed to the extract_emails function. This function extracts all email addresses from the given text and returns them joined by a space. So it returns \"tom@gmail.com\".\n\n1. This extracted email \"tom@gmail.com\" is then passed to the mask_information function.\n\n1. The mask_information function masks all but the last 4 characters of sensitive information like emails and phone numbers. It replaces the characters before the last 4 with asterisks (\\*).\n\n1. So it masks \"tom@gmail.com\" to \"tom@g\\*\\*\\*\\*\\*.com\".\n\n1. This masked string \"tom@g\\*\\*\\*\\*\\*.com\" is returned as the final output.\n\nSo in summary, the extract_emails function extracts the email from the input string, and the mask_information function then masks all but the last 4 characters of the extracted email, giving the final output as \"tom@g\\*\\*\\*\\*\\*.com\".", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "-. .-.. .--.   .. ...   -.-. --- --- .-..", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Based on the functions defined in the codebase, here are the steps:\n\n1. The input string \"NLP is cool\" is passed to the string_to_morse() function.\n\n1. string_to_morse() converts each character in the input string to its corresponding Morse code representation based on the mapping defined in the morse_dict dictionary.\n\n1. It returns a string with Morse code representations of each character separated by a space.\n\n1. For example, \"N\" is converted to \"-.\" , \"L\" to \".-..\" and so on.\n\n1. Finally, the output printed will be the Morse code equivalent of the input string:\n   \"-.-. .- - .- ... .\"\n\nSo in summary, the final output printed is the Morse code representation of the input string \"NLP is cool\".", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "{\"i\": 1, \"love\": 1, \"nlp\": 1}", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. The detailed_word_counter function takes a string as input and returns a dictionary with word frequencies as a JSON string\n1. The input string \"I, love, nlp!\" is passed to this function\n1. The function first normalizes the string by converting to lowercase and removing punctuation\n1. It then splits the string into words: \\['i', 'love', 'nlp'\\]\n1. It initializes an empty dictionary to store word frequencies\n1. It iterates over the words and increments the count for each word in the dictionary\n1. After counting all words, it returns the dictionary as a JSON string\n\nSo the final output would be:\n\"{\"i\":1,\"love\":1,\"nlp\":1}\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "!dlroW", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. The input string is \"World!\"\n1. remove_accents() is called on the input string. This function removes any accents from characters.\n1. The resulting string is passed to reverse_string(). reverse_string() reverses the order of characters in the string.\n1. Therefore, the final output printed will be \"!dlroW\"\n\nSo the final output is \"!dlroW\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "NlP Is InTeReStInG.", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "The final output of the code is:\n\nnLP iS inTeresTing.\n\nHere's how it works:\n\n1. The input string \"NLP is interesting.\" is passed to the word_with_mixed_case() function.\n\n1. This function takes each word in the input string, and alternates between uppercase and lowercase for each character in the word.\n\n1. The first character is uppercased, second is lowercased, third is uppercased and so on.\n\n1. This transformation is done for each word individually.\n\n1. The transformed words are joined back into a string separated by spaces.\n\n1. Therefore, the output string has \"NLP\" as \"nLP\", \"is\" as \"iS\", and \"interesting\" as \"inTeresTing\".\n\nSo the final output after applying the word_with_mixed_case() transformation on the input string is \"nLP iS inTeresTing.\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "i aam_a_n_l_per. ***", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. Input string: \"I aamANLPer. password\"\n\n1. camel_to_snake() function is called on the input string. This function converts camelcase strings to snake_case.\n   So the output would be: \"i_aam_anlper. password\"\n\n1. obfuscate_sensitive_data() function is then called on the output of the previous step. This function replaces sensitive keywords like \"password\" with \"\\*\\*\\*\".\n\n   The sensitive keyword list in this function contains \"password\".\n\n   So the final output after applying both functions would be:\n\n   \"i_aam_anlper. \\*\\*\\*\"\n\nSo in summary, the final output after applying the given functions on the sample input string is:\n\n\"i_aam_anlper. \\*\\*\\*\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "NLP", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "The final output is \"NLP\".\n\nHere's what the code is doing:\n\n1. filter_words takes a string and filters out words based on length and optional prefix.\n\n1. The input string passed is \"NLP A is cool\".\n\n1. The length criteria specified is 3.\n\n1. Of the words in the input string, only \"NLP\" satisfies the length criteria of 3 characters.\n\n1. The prefix parameter is empty string by default.\n\n1. \"NLP\" also satisfies the prefix criteria as the prefix is empty.\n\n1. Only \"NLP\" matches both the length and prefix criteria.\n\n1. filter_words returns a space separated list of words that match. Here it is just \"NLP\".\n\nSo the final output printed is \"NLP\".", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "{\"nlp\": 1, \"a\": 1, \"cool\": 1}", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "The generate_word_cloud_data function takes a string as input, removes common stop words like 'the', 'is', 'a' etc. and counts the frequency of remaining words. It returns a JSON serialized dictionary with words as keys and their frequencies as values.\n\nSo for the input string \"the NLP a is cool\", after removing stop words, the remaining words are \\[\"NLP\", \"cool\"\\].\n\nThe word count dictionary will be:\n{\"NLP\": 1, \"cool\": 1}\n\nThis dictionary is then JSON serialized and returned. So the final output will be:\n\n\"{\"NLP\":1,\"cool\":1}\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": ":$5n\u0002\u001c\u001el9=l3!#<", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "The xor_encrypt() function encrypts a string using XOR encryption with a given key.\n\nIt takes each character in the input string and performs XOR (exclusive OR) operation on it with the character at the same index in the key.\n\nFor characters that are not alphabets, it simply copies them to the encrypted string.\n\nIn this case:\n\nInput string: \"the NLP is cool\"\nKey: \"NLP\"\n\nGoing through each character:\n\n't' XOR 'N' = 'd'\n'h' XOR 'P' = 'j'\\\n'e' XOR 'L' = 'f'\n' ' XOR 'N' = ' '\n'N' XOR 'P' = 'M'\n'L' XOR 'L' = 'L'\n'P' XOR 'P' = 'P'\n' ' XOR 'N' = ' '\n'i' XOR 'P' = 'g'\n's' XOR 'L' = 'r'\n' ' XOR 'N' = ' '\n'c' XOR 'P' = 'e'\n'o' XOR 'L' = 'n'\n'o' XOR 'L' = 'n'\n'l' XOR 'P' = 'k'\n\nJoining all the encrypted characters, the final output is: \"djf MLLPg r coolk\"\n\nSo in summary, the xor_encrypt function encrypts the input string \"the NLP is cool\" with key \"NLP\" and returns \"djf MLLPg r coolk\" as the final output.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "P: 1 ->  : 2 -> t: 2 -> e: 2 -> r: 1 -> .: 1 -> N: 1 -> g: 1 -> s: 2 -> i: 3 -> n: 2 -> L: 1", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "The string_variant_generator function takes an input string and returns a mapping string showing the character frequencies in the format \"character: count\".\n\nIt first splits the input string into words. Then it populates a dictionary with character frequencies by iterating over each character in the input string and incrementing the count if the character is already present in the dictionary, or setting count to 1 if not.\n\nIt then replaces each character in the input string with that character surrounded by parentheses followed by its count from the character frequency dictionary (e.g. \"i(2)\").\n\nFinally, it joins all the character mappings into a single string separated by \" -> \" and returns it.\n\nTherefore, for the input string \"NLP is interesting.\", the final output would be:\n\n\"N(1) -> P(1) -> L(1) -> i(2) -> s(1) -> (1) -> i(4) -> n(2) -> t(2) -> e(3) -> r(2) -> g(1)\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "2032-12-01aaabbbb", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. remove_accents() removes any accents from the characters in the input string. This returns a string without accents.\n\n1. expand_string() expands any compressed substrings like 'a3b4' to 'aaabbb'. This returns a string with compressed parts expanded.\n\n1. get_dates() extracts any date patterns like '2032-12-01' and returns them as a string separated by space '2032-12-01'.\n\n1. These extracted dates along with the returned string from previous steps are concatenated and added to s.\n\n1. Finally s is printed which will contain:\n\n2032-12-01 Hello word. NLP is interesting aaabbb\n\nSo the final output is 2032-12-01 Hello word. NLP is interesting aaabbb", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "NLP is interesting ,! xxx-xxx-1100 T*m@gmail.Com7", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. highlight_keywords function puts keywords like \"NLP\" in uppercase in the input string. So the string becomes \"NLP is interesting ,! 18230221100 tom@gmail.com\"\n\n1. transform_after_symbol function transforms the first character of each word to uppercase if it occurs after punctuation like '.', '?' etc. So the string becomes \"NLP Is interesting ,! 18230221100 tom@gmail.com\"\n\n1. hide_info function hides personal information like email (replacing all but last part) and phone numbers. So the string becomes \"NLP Is interesting ,! xxx-xxx-1100 \\*\\*\\*\\*\\*\\*\\*\\*@gmail.com\"\n\n1. count_syllables counts the number of syllables in each word and returns it as a string. For the word \"interesting\", it will return \"3\".\n\n1. Finally, the syllable count is concatenated to the end of the modified string.\n\nSo the final output is:\n\"NLP Is interesting ,! xxx-xxx-1100 \\*\\*\\*\\*\\*\\*\\*\\*@gmail.com3\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "3_PLN_POS0 2_sI_POS1 12_DOTgnItsErEtnI_POS2 6_tOnnAc_POS3", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. expand_contractions is called on the input string \"NLP is interesting. can't\". This function expands the contraction \"can't\" to \"cannot\". So now the string is \"NLP is interesting. cannot\".\n\n1. complex_string_processing is called on the above string. This function:\n\n   - Reverses each word\n   - Capitalizes vowels in each word\n   - Prepends the word length to the word\n   - Replaces punctuation with predefined strings\n\nAfter applying all the transformations, the final output string is:\n\n\"3PL nis 5.4tenresnit tnnooc\"\n\nSo the final output of the given code is \"3PL nis 5.4tenresnit tnnooc\".", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "Character: L, Type: consonant, Frequency: 15", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here is the step-by-step working:\n\n1. count_vowels(Input_string) counts the vowels in the input string \"NNLP is interesting.\". It returns \"9\" as there are 9 vowels.\n\n1. first_non_repeated(Input_string) finds the first non-repeated character in the input string. It returns \"i\" as \"i\" is the first character that is not repeated.\n\n1. detailed_character_info(s) takes the string \"i\" as input and generates detailed character information about each character in the string. Since the string \"i\" contains only one character \"i\", the output is a single string with details about \"i\":\n   \"Character: i, Type: vowel, Frequency: 1\"\n\n1. Finally, we are printing the output of step 3 along with the output of step 1.\n   So the final output is:\n   \"Character: i, Type: vowel, Frequency: 1\"9\n\nSo in summary, the functions are called sequentially on the input string, and the final outputs of individual functions are concatenated to give the overall output.", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "Interesting.[1]1", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. to_title_case(Input_string) converts the input string to title case. So \"NNLP is interesting. 1\" becomes \"NNLP Is Interesting. 1\"\n1. find_longest_word returns the longest word. The longest word in the title cased string is \"Interesting\".\n1. extract_numbers extracts all the numbers as a list. For the input string it is \\[\"1\"\\].\n1. count_word_occurrence counts the number of occurrences of the word \"is\". It occurs 1 time.\n1. We concatenate the longest word, the extracted numbers and the word count.\n   So the final output is:\n   \"Interesting1\" + \"1\" = \"Interesting11\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "False{\"F\": || 1, || \"a\": || 1, || \"l\": || 1, || \"s\": || 1, || \"e\": || 1}", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. Input string is \"my you!\"\n1. replace_substrings function replaces \"my\" with \"your\", so the string becomes \"your you!\"\n1. has_unique_characters checks if each character occurs only once. It returns \"True\" since all characters occur only once.\n1. character_frequency counts the frequency of each character and returns a JSON string like '{\"y\": 1, \"o\": 2, \"u\": 1, \"r\": 1}\".\n1. validate_string validates the JSON string and returns \"True\" since length criteria matches.\n1. custom_string_splitter splits the string on space, returning \"y||o||u||!\".\n\nConcatenating the return value of steps 5 and 6 gives the final output as \"True||y||o||u||!\".", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "i EVOL !PLN", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Okay, here are the steps:\n\n1. Input string: \"I love nlp!\"\n1. switch_case() function switches the case of each character. So it returns \"i LOvE NLP!\"\n1. clean_string() function removes any digits/punctuation. So it returns \"i LOvE NLP\"\n1. reverse_words_in_string() reverses each word. So it returns \"NLP evoL i\"\n\nTherefore, the final output is:\n\"NLP evoL i\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "I, love, nlp!, large, large, JohnFalse", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. string_splitter() splits the input string \"I love nlp! large large {name}\" on spaces. It returns \"I||love||nlp!||large||large||{name}\".\n\n1. custom_string_formatter() takes this split string and replaces the placeholder \"{name}\" with the value from the dictionary, which is empty. So it returns \"I||love||nlp!||large||large||\".\n\n1. locate_substring_with_context() searches this string for the substring \"large\" and returns a list of tuples with the matched index and surrounding context strings. It will return \\[(4, 'large||large||'), (9, 'large||')\\]\n\n1. contains_substring() checks if the string contains the substring \"large\". It will return True.\n\nSo the final output printed will be:\n\n\\[(4, 'large||large||'), (9, 'large||')\\]True", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "level,deed", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. The input string is passed to the find_palindromes() function.\n\n1. In find_palindromes(), it splits the input string into words, cleans each word by removing punctuation and converting to lowercase.\n\n1. It checks if each cleaned word is equal to its reverse. If yes, it is a palindrome and is appended to the palindromes list.\n\n1. It returns a comma separated string of the palindromes list.\n\n1. In this case, \"level\" and \"deed\" are palindromes.\n\n1. So the output would be: \"level,deed\"\n\nSo the final output of the code is: \"level,deed\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "iuuq://xxx.hpphmf.dpn", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Based on the code provided, here are the steps:\n\n1. The function extract_urls() is called on the input string \"[http://www.google.com \u2197](http://www.google.com) , tom@gmail.com\"\n\n1. extract_urls() will extract all URLs from the given string and return them joined by a \" \" separator. So it will return \"[http://www.google.com \u2197](http://www.google.com)\"\n\n1. This extracted URL string \"[http://www.google.com \u2197](http://www.google.com)\" is passed to the caesar_cipher_encrypt() function along with a shift of 1.\n\n1. caesar_cipher_encrypt() implements a Caesar cipher encryption that shifts each character by the provided shift value. With a shift of 1, each character is shifted by 1 position.\n\n1. So the encrypted version of \"[http://www.google.com \u2197](http://www.google.com)\" with a shift of 1 will be \"ijuu://xxx/hphhmf/dpn\"\n\nTherefore, the final output printed will be:\n\"ijuu://xxx/hphhmf/dpn\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "I1 1l1o1v1e1 1N1L1P1", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. simple_correction function is called on the input string \"I lovve NLP NLP\".\n   It corrects consecutive repeating characters and returns \"I love NLP NLP\".\n1. remove_duplicate_words function is called on the output of step 1.\n   It removes duplicate words and returns \"I love NLP\".\n1. run_length_encode function is called on the output of step 2.\n   It encodes the string using run length encoding and returns \"I1 l1o1v1e1 1N1L1P1\".\n\nTherefore, the final output of the code is \"I1 l1o1v1e1 1N1L1P1\".", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "I Am a Nlper. YOGA", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. Input string is \"I aam a NLPer. Yoga\"\n\n1. simple_correction(Input_string) removes duplicate adjacent characters.\n   It returns \"I am a NLPer. Yoga\"\n\n1. title_case(s) titles cases the words while keeping exceptions like 'a', 'to' in lowercase.\n   It returns \"I Am A NLPer. yoga\"\n\n1. highlight_keywords(s) makes keywords like 'NLP', 'Yoga' uppercase as they are present in the list of keywords provided earlier.\n   It returns \"I Am A NLP ER. YOGA\"\n\nSo the final output is \"I Am A NLP ER. YOGA\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "3False", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the step-by-step workings:\n\n1. string_normalizer() converts the input string to lowercase and removes punctuation, extra spaces.\n   Result: \"i love nlp\"\n\n1. string_splitter() splits the string into a list of words separated by space.\n   Result: \"i\", \"love\", \"nlp\"\n\n1. custom_find_first_occurrence() finds the index of first occurrence of \"l\" in the list.\n   Result: 1 (index of \"love\")\n\n1. encode_string() performs a Caesar cipher encryption on the word at the returned index (i.e. \"love\") with a shift of 1.\n   Result: \"mpwf\"\n\n1. is_palindrome_ignore_chars() checks if the input string is a palindrome after ignoring commas, period and exclamation.\n   Result: \"True\"\n\n1. Concatenating the results from steps 4 and 5:\n   \"mpwfTrue\"\n\nSo the final output is \"mpwfTrue\"", "evaluation": "exam"}
{"query": "What is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "gt": "T H E N L P I S C O O L 1 9 8 2 / 0 3[1, 9, 8, 2, 0, 3]", "prompt": "Now you are given a code base consisting of a large amount of functions and the corresponding comment. In the end, I will call some functions defined in the code base. Please carefully read these codes and comments and answer the question.<long input>\n\nWhat is the final output of the code? Notice that the comment and examples provided in each function may help solve the problem. An Example: <E> So the final output is \"NLP is insteresting\" </E>\nLet's think step by step\n", "Claude-instant-100k_pred": "Here are the steps:\n\n1. remove_multiple_spaces() removes multiple spaces in the input string. So it returns \"the NLP is cOol 1982/03\"\n1. concatenate_with_separator() joins the words in the string with a space. It returns \"the NLP is cOol 1982/03\"\n1. extract_numbers() extracts all numbers and returns \"\\[1982, 03\\]\"\n1. get_dates() extracts any date patterns and returns \"1982/03\"\n1. to_title_case() converts the string to title case, keeping exceptions lowercase. It returns \"The Nlp Is cOol 1982/03\"\n\nFinally, we concatenate all the outputs and print. So the final output is:\n\n\"The Nlp Is cOol \\[1982, 03\\] 1982/03\"", "evaluation": "exam"}
